Upload
others
View
8
Download
2
Embed Size (px)
Citation preview
22
Companies are facing key challenges with respect to workforce transformation
• Over 80% of the Job Roles that will be filled over the next decade do not exist yet• Companies have a very limited understanding of employees’ skills inventory and upskilling potential• Companies fear the entry of technology giants such as Google, Microsoft, Amazon without understanding the specific portfolio
of their work• CIO organizations as a whole, its reporting structure, and the portfolio it owns are key priorities • Technology is highly impacting risk management teams. Physical, Cyber, Customer, Data, Financial and all other forms of risk are
a key priority• Aligning internal roles to the ever changing external job roles is emerging as a key challenge• Companies have a large location footprint due to globalization, but locations aren’t leveraged optimally• Training programs are not tied to career outcomes. As a result, companies report less than 20% uptake of the offered training
programs• University relationships have not often progressed beyond recruiting interns• Mapping personas and building a brand to attract Digital talent pool is becoming extremely critical• The taxonomies of key technology areas such as Cloud, AI, Cybersecurity are rapidly changing (for example, we have mapped 26
job roles on cloud)
By nurturing, developing and attracting the right talent, HR can now impact the top line as AI driven companies disrupt industries
Organizational Priorities and Challenges (across industries)
33
Executive Summary
Global talent pool trending including availability of talent in certain markets to fill mission critical roles
Broad and deep understanding of how peers and near peers are performing and competing for talent
Forward looking view into emerging jobs or capabilities & how they may impact your workforce strategy
Diverse talent pipeline across critical job families and locations
Global location and ecosystem analysis
Ability to source critical talent and develop talent pipeline strategies in key talent markets, globally
Talent Availability Talent Competitors Emerging Talent Talent Pipelines High Cost/ Low Cost Talent Locations
Location Strategy & Planning
Ø In today’s competitive environment, companies are making major shifts in business strategy and are looking for tools to help them visualize the potential impact of shifts over time in the areas of:
Ø Zinnov’s products and service offerings provide a significant advantage to its customers through: • unlimited access to the capabilities built into Draup – an AI- enabled platform mining the most
comprehensive, publicly available data sets across the globe • their team of researchers, consultants and machine learning scientists who will optimize the what and
how of Draup’s capabilities
Source : DRAUP
4
4 4
Enabling Business & HR Leaders to Answer Critical Talent Capabilities Questions Using Data and Science
• Understand talent demand and availability of job roles by job families or defined skillsets
• Supply-Demand analysis and gap estimation for any job roles, job families or defined skillsets
• Evaluate and compare current salary costs and growth rates across global locations by job roles
• Identify emerging skillsets where the talent with emerging skillsets are located
• Understand best practices for organization and job role structure for emerging functions
Emerging Talent MarketsWorkforce Supply & Demand Planning• Understand talent patterns and trends based on your
company’s “mission critical” roles• Global location simulation for understanding the direct and
total talent costs across locations for any job roles, job families or defined skillsets
• Talent peers analysis including strategic intent analysis, technology tools adoption, globalization footprint and hiring patterns
• Detailed diversity trend analytics-driven dashboard organized by locations, job families, roles and skills
Talent Patterns and Competitive Trend AnalysesEnabling Informed Workforce Strategy and Executable Talent Planning
Strengthening and Augmenting Recruiting Capabilities
• Discover best fit candidates based on location, job roles, skillsets, defined “hiring fit” criteria
• Identify and pursue the right candidates that are the most likely to join your company
• Identify best fit profiles based on personality essence and psychographic analysis
• Understand detailed characteristics of potential hires including job progression, stack capabilities, tenure type
• Millennial hire mining characteristics, including analysis of signals and attributes from social media
• Learn the best method to engage potential hires based on engagement guidance derived from psychographic and interest analysis
Talent SourcingTalent Identification• Ability to mine and generate “movers and
shakers” analyses based on your company’s unique criteria
• Visibility into metrics to build business cases –including data points like cost components, talent pool availability, and probability to join
Talent Pipeline Trend Analysis
Source : DRAUP
5
5 5
Enhancing and Enabling Workforce Strategy & Transformation
Location Intelligence
• Talent Pool─ Employed Talent Pool and
Growth Forecast─ Talent Demand and
Forecast• Cost & Salaries
─ Fully Loaded FTE Cost─ Average Salary for any
Role and Location• Hiring Metrics
• Competitive Intensity• Talent Insights
• Top Skills and Tools Experience
• Major Certifications• Gender and Ethnic
Diversity • Talent Supply
• University Talent Pool• University Curriculum and
Ratings
• Global Workforce ─ Globalization Footprint─ Hiring Patterns – Top Skills,
Roles and Locations─ In Demand Job Roles
Mapping─ In Demand Skills Mapping
• Business and Financial Performance
─ Subsidiaries, Business Units & Products
─ Strategic Intent Analysis─ Financial Performance
• View of Technology Stacks • Outsourcing Partnerships• Partnerships and Investments• Recent Events and News
(Signals)• Executive Job Movements• Partnerships
• Professional Bio─ Detailed Professional
Experience─ Education Background
• Interests─ Personal Interests and
Insights─ Influencers
• Psychographics─ Psychographic Analysis to
Predict the Right Fit─ Communication and
Engagement Guidelines• Professional Social Profiles –
including LinkedIn, GitHub, Stack Overflow, Dribble, Stat Exchange
• Derived Metrics to predict the potential to Hire based on:
─ Relocation Propensity ─ Probability of Promotion─ New/Emerging Capability
• Skills and Tools Experience
View 40+ attributes for any Location and Job role
View 2,500+ attributes that provide multi-dimensional analysis of peer companies
Peer Intelligence Smarter Sourcing & Recruiting
View 50+ attributes detailing hiring resume for 2 Mn+ Profiles
Enabling Talent Management teams with a comprehensive integrated view of the global talent ecosystem
• Location Analysis• Talent Insights for Job roles• Talent Insights and Trends around
Emerging Technologies• Talent and HR trends and Thought
Leadership• Peer group macro organization
structure analysis• Peer Global Micro hub Analysis• Career Progression Analysis• Innovation in community colleges
and smaller universities• Startup Analysis• Executive Leadership Analysis
Consulting On Demand
Strategic Decision Support & Access to Syndicated Reports (BrainDesk)
6
6
Draup has the ability to analyze deep characteristics of all emerging technology stacks. Characteristics of Cloud Talent
Note: DRAUP’s Talent Simulation Module: The locations mentioned above are major locations with relevant Cloud Engineer talent and are based on presence of global as well as local Software,
Information Technology, Internet and Research companies. Installed Talents in IBM & Accenture are included under IT Services.
~70% TalentConcentrated in
Top 15 Locations
IT Services Industry Employs
~40% of Talent Pool
Software & Internet Industry Employs
~35% of Talent Pool
Telecommunications,
Banking & Financial
Services Industries each
employs ~5%
Talent Pool
San FranciscoSan Mateo
Sunnyvale
Mountain View
San JoseOrange County
Irvine
San Diego
Tempe
ProvoBoulder
Denver
Colorado Springs
Richardson
HoustonSan AntonioFlorida
Tampa/St. Petersburg
Birmingham
DurhamCharlotte
LouisvilleCincinnati
Columbus
Chicago
Pittsburgh
Nashua
BostonConnecticut
New York City
BrooklynPrincetonNew JerseyPhiladelphia
BaltimoreWashington D.C.
HerndonAlexandria
Talent Hotpots Top Talent Regions
Helena
Boise
Oklahoma City
Lincoln
Fargo
Rapid City
Tucson
Milwaukee
WEST COAST REGIONCloud Focus Companies Major
Employers: Google, Oracle,
VMware, Salesforce.com, Facebook TEXAS AREAEnergy & Utilities, Telecom Hotspot
Major Employers: Exxon Mobil, Halliburton,
AT&T
EAST COAST REGIONSoftware & BFSI Hub
Major Employers: IBM, Verizon,
Citigroup, American Express
SEATTLE AREATech GiantsMicrosoft, Amazon and Expedia
have
12 R&D centres primarily HQs
SOUTHERN COSAT REGIONEmerging Software Hotspot
Major Employers: Amadeus, Ultimate
Software Group, ADP
EAST CENTRAL AREASoftware Hotspot
Major Employers: Fiserv, Allscripts,
BestBuy, Strattec Security
ATLANTA AREATelecom Hotspot
Major Employers: NCR Corporation, COX
Communications
DENVER AREAIT Services Hotspot
Major Employers: CSG
International, TTEC
BOSTON AREAHealthcare Hub
Major Employers: GE Healthcare,
Boston Scientific, Akamai
Technologies
77
Career Progression Analytics: A number of Success Stories of Professionals with non-tech background transitioning into AI/ML through micro certifications
Note : The above information is based on data provided by the DRAUP Proprietary Database.
Hilary DotsonData Scientist at Centre For Human Capital Innovation (CHCI)Education: Ph.D. in Sociology
Nicole H. RomanoCTO & co-founder at StealthEducation: Ph.D. in Materials Science & Engineering
Surya Prakash ManpurData Scientist @ RealPage, Inc.Education: (B.Tech.) Electronics and Communications Engineering
Researcher in Polymer Science
Data Scientist
Data Science Fellow at Insight Data Science + Certification course on statistics and programming
Quality Assurance Analyst - III
Data Scientist
Certificate Program in Big Data Analytics and Optimization (CPEE) Data Science, Big Data Analytics
Teaching Associate in Sociology
Data Scientist
Certificate Program in Exploratory Data Analysis, R-Programming and Data Science.
Postdoctoral Researcher in Radiology
Data Scientist
Data Science Fellow at Insight Data Science
Regulatory Compliance Investigator
Data Scientist
Certificate Program in Python Programming, Data Visualization, SQL and Database
Research Assistant in Biomedical Engineering
Data Scientist
Post Graduate Fellow at Insight Data Science
Sebastien DeryMachine Learning at AppleEducation: Master of Engineering (M.Eng.) in Medical Engineering
David K.Data Science - Analytics at Gemini.comEducation: Bachelor’s Degree in Business Administration
Sarah KefayatiData Science Fellow at Insight Data ScienceEducation: Ph.D. in Medical Physics
88
A platform about potentials and possibilities: Successful Transition to Data Scientist/Big Data Analyst role from various traditional roles
Transformation - Certification/Programmes
• Creative Applications of Deep Learning withTensorFlow.
• Neural Networks for Machine Learning• Deep Learning A-Z: Hands On Artificial Neural
Networks
• PG diploma in Management(PGDM), Analytics and Marketing
• SAS Enterprise Minor Certification
• AWS Certified Developer Certification• Cloudera Certified Associate (CCA) Spark and
Hadoop Developer
Sample Profiles Past Experience
• Business Analyst (2014)• Credit Underwriter
(2013)• Loan Specialist (2011)
• Business Intelligence Analyst (2015)
• Developer VBA (2011)• Account Specialist
(2010)• Offset Printer Operator
(2008)
Workload CharacteristicsRodrigo DomingosBusiness Intelligence Specialist, Travelers
Khushbo Makhija
Data Analyst, Edelweiss Tokyo Life Insurance
Kwasi Opoku
Team Lead Big Data, MetLife
Data Scientist
Data Analyst, Insurance Policies
Team Lead, Big Data Analytics
Creating Machine Learning models to increase the competitive advantage of the company.Working on the Information management, data architecture, dashboard development for Brazilian and United States
Performed exploratory data analysis and identified key factors, reasons and trends in surrender policies in R and Tableau
Skilled in Hadoop ecosystem technology stack , Strategic Planning, in depth knowledge in big data and BI tools
• Claim Business Analyst (2015)
• Claim Processor (2013)• Content Analyst (2013)
New Age Roles
99
Enable untapped talent pool: ~12.8 million working mothers have taken career breaks of 2+ years during their professional life cycle
Women who are economically inactive (0-2 yrs)
Women who started looking for jobs
oighs
Women who returned to work after career break
Women who do not return to labor force
Full Time Part Time
Jobs based on their skill set
Jobs not based on their skill set
Jobs based on their skill set
Jobs not based on their skill set
Women who are content working part time
Women who would like to work full time
63% 37%
30%70%
60% 40%
40% 60%
24% 76%
82% 18%
~12.8 Mn2018 -US – Mothers - Career Break
Note: The represented data is analyzed from DRAUP’s Proprietary Talent Module as well as primary interviews from industry stakeholders
1010
Unique job roles : 7 Primary Skills and job roles were shortlisted and analysed within Big Data & Data Science engineering
Unique Roles Titles Technical or Conceptual Skills Description
Note : DRAUP’s proprietary talent module was used to analyse jobs by job roles and skill type
Analyst - Data Management
Maintains and manages the database; Responsible for performing quality checks on datasets; Ensures correct data schema and syntax; Filters and cleans data"
Database Analyst, Data Management Analyst, Database Developer, Database Administrator
1.
Data Architect
Designs and implementing the technical architecture; Defines and designs data systems, services and technology solutions; Implements and administers data infrastructure
Data Architect, Tech Lead Data Platform, Tech Lead Data Modelling
2.
Big Data / Hadoop Administrator
Supports the Hadoop infrastructure and ensures availability; Responsible for node-cluster configuration, deployment and capacity planning; Monitors and maintains clusters and tunes performance; Responsible for administering YARN and providing support for running and monitoring MapReduce jobs
Creates data pipelines to move and transform data; Responsible for performing transformations to aggregate disparate data volumes into data lakes; Manages data from different sources; Provides support, maintenance, monitoring and troubleshooting for data warehouse processes
Data Warehouse Engineer
Data Engineer, Data Warehouse Engineer, Data Warehousing Specialist, Data Developer, Hadoop Developer, Spark Developer, Hadoop Engineer, Spark Engineer, Scala Engineer, Scala Developer
Hadoop Administrator, Data Administrator, Big Data DevOps Engineer, Hadoop Platform Engineer
3.Database
Engineering
apache, azure, distribute systems, flume, google cloud, gradle, integrations, java j2ee,
database architecture, relational databases, data cleaning, data manipulation, tableau, power bi, excel
hadoop, flume, YARN, mongodb, dynamodb, mapreduce, devops, hbase, hdfs, AWS
hbase, amazon web service, kafka, spark, cassandra, dynamodb, flume, gradle, graph, hadoop, jmeter, json
Skill Definitions
1111
Unique job roles : 7 Primary Skills and job roles were shortlisted and analysed within Big Data & Data
Science engineering
Develops algorithms for conversational
interfaces such as chatbots; Identifies the
modes by which speech can be converted to
data; Develops conversational interfaces
using bot frameworks and platforms
Chatbot Developer, Chatbot Engineer, Conversational UI Specialist, Speech Scientist, Speech Researcher, Speech Algorithm Scientist, Speech Algorithm Engineer, Speech Algorithm Researcher, Machine Learning Engineer - Speech, Machine Learning Scientist - Speech, AI Engineer -Speech, AI Researcher - Speech, Machine Learning Engineer - Conversation, Machine Learning Scientist - Conversation, AI Engineer -Conversation, AI Researcher - Conversation
Applied Data Scientist - Speech
Note : DRAUP’s proprietary talent module was used to analyse jobs by job roles and skill type
Applied Data Scientist - Vision
Computer Vision Scientist, Computer Vision Engineer, Algorithm Engineer - Computer Vision, Computer Vision Engineer, ADAS Engineer, Vision Engineer, Perception Engineer, Deep Learning Scientist, Deep Learning Engineer
Develops algorithms for vision-based
applications such as image or object
recognition applications; Designs vision
algorithms for mapping, localization, scene
analysis, object detection and classification;
Develops a perception based solution
integrating multiple sensing devices within
the size, weight and power (SWaP)
5.Applied AI
Analyses and interprets data (both structured
and unstructured) and generates prescriptive
and predictive insights; Responsible for
generating insights from raw data using
inferential and predictive models; Responsible
for developing new analytical models for the
organization
Data Scientist, Applied Scientist, Data Researcher, Applied Researcher, Data Modeling Scientist, Data Modeling Specialist, Data Modeling Engineer, Data Mining Scientist, Data Mining Specialist, Data Mining Specialist, Algorithm Scientist, Algorithm Engineer, Algorithm Specialist, Machine Learning Scientist, Machine Learning Engineer, Machine Learning Researcher, NLP Scientist, NLP Researcher, NLP Engineer
Data Scientist4.
Unique Roles Titles Technical or Conceptual Skills Description
OpenCV, Tensorflow, Pandas, 3D
Modelling, Adaptive Thresholding,
Caffe, Convolutional Neural Network
Dialogflow, API.ai, Wit.ai, Microsoft
Bot Framework, Bayes Rule,
Bidirectional RNN, Chomsky
Hierarchy
Classification, clustering, decision trees,
dimensionality reduction, logistic
regression, SVM, natural language
process, predictive analytics,
Skill Definitions
1212
Global Micro Hubs: Smaller locations have high talent scalability potential due to increased government spending to strengthen infrastructure, socio-economic condition and start-up & university ecosystem
130+Talent Hotbeds
~20% Of AI Talent is employed across
tier-2 locations in 2018
37Countries will be home to 1Mn Machine learning developers by
2030
Mumbai
Delhi
Atlanta
Los angels
Chennai
Pittsburgh
Pune
Paris
Cambridge
KlonAmsterdam
PhiladelphiaSan Jose
HoustonTampa
Lyon
Orlando
Denver
Shenyang
Singapore
Hyderabad
Detroit
Green BayMinneapolis
Hong Kong
Guangzhou
Nanjing
ShenzhenGainesville
Guatemala
RecifeBogota
Campinas
SantiagoSau Paulo
Lima, peru
Lagos
Durban
Accra
Nairobi
Morocco
Colombo
Jakarta
BrisbaneSydney
Melbourne
AdelaidePerth
SeoulKawasaki
Chongqing
Chengdu
Jilin
Buenos Aires
Ho-chi-minhVizag
Surat
Ahmedabad
Dallas
San Diego
Phoenix
Coimbatore
Stockholm
Chandigarh
BucurestCluj
Gdansk
Kolkata
Cairo
Dubai JaipurSan Francisco
Seattle
New York
Boston
LondonMunich
Tel Aviv
Tokyo
Beijing
ShanghaiBangalore
Hotbeds 2018
Emerging Hotbeds
Source: Zinnov Global Machine Learning talent forecasting modelerZinnov analysis of university programs, fresh ML graduates, digital initiatives of Government and Enterprises, StartupsGeneric Data Science Talent pool not considered as there is noise in the data
Employed AI & Big Data Talent Pool
INDIA
• Smart city project
• Heavy infrastructure spend
• Emerging start-up ecosystem
AFRICA
• Social Start-ups’ Impact
• iHub maturity
• Investment by China
SOUTH AMERICA
• Brazil tier-2 cities
• Corruption impact in Brazil
• Mexico stepping up tech impact
USA/Canada
• Higher real estate cost in East
• Rewiring of Auto Industry
• Less regulation in South
• Move towards south and west
CHINA
• Megalopolis initiative
• One-child policy impact
• Migration towards west
EUROPE
• Brexit’s impact – Shift to Germany
• Job Demands in Eastern Europe
• Companies setting up new EU HQs
1313
Ability to analyze niche talent pools DRAUP analysed Economist talent across 70+ locations to identify top 8 hotspots
Seattle
Los Angeles
Dallas/Fort Worth
Area
Mexico City
Toronto
New York CityWashington D.C.
Buenos Aires
Lisbon BarcelonaRome
MilanZurich Budapest
Paris
Warsaw
IstanbulBucharestZagreb
Munich
Berlin
Copenhagen
Stockholm
OxfordLondon Brussels
Cambridge
Bengaluru
MumbaiPune
Jakarta
Hong Kong
Melbourne
Bengaluru
Taipei
ShanghaiSakyo-ku-Kyoto
Note: DRAUP’s Talent Simulation Module: The locations mentioned above are major locations with relevant Econometric talent and are based on presence of global as well as local Banking, Financial services and insurance companies. The data is not exhaustive
GLOBAL TALENT: ~30,000
70+ Locations Analysed
1414
SecurityArchitect
NetworkSecurityEngineer
Cloud Security DevOps Engineer
BackupAdministrator Risk Analyst Risk
ManagerVulnerability
Analyst DLP EngineerAnalyst End
Point Security
SIEMEngineer
SecurityOperations
Lead
IncidentResponder
Analyst Identity and
Access Management
AccessControl
Administrator
ActiveDirectoryEngineer
IAMEngineer
Washington DC
London
New York
Bengaluru
San Francisco
Over 63% of the current demand is distributed across Active Directory Engineer & Incident Responder roles followed by Security Architect and Risk Manager roles
LEGEND
Cell ColourDemand of the Role at corresponding
location
Note: The above represented heat map is created on basis of number of jobs posted at each location in job portals like Public LinkedIn, Indeed, Monster, Naukri, and others updated in Sep, 2018
Very Low
Low Med High Very High
Major Cybersecurity Job Roles In Demand Across Top 5 MSAs
1515
~21% of the global cybersecurity talent pool is distributed across top 10 MSAs globally. Washington D.C. employs the highest cybersecurity professionals globally
Top Locations across the globe with major Cybersecurity talent Composition
~500KDirectly employed in technology companies*
~19%of the global cybersecurity talent is employed
across top 11 cities in the US
~60%Cybersecurity talent have 10+ years of
experience
12,0007,000
9,000
24,000
10,000
13,000
8,000
14,000
23,000 10,000
20,000
Bay Area New York
London, UK
Bengaluru
MumbaiWashington D.C.
Toronto, Canada
UAE
Singapore
Sydney
Paris
ItalyMalaysia
Johannesburg
Chicago Area
Los Angeles
Denver Area
Detroit
Houston, Texas
Minneapolis
New Delhi47,000
15,000
Note: The represented data has been collected from multiple job portals, news articles and Draup Proprietary Database updated as of Sep, 2018; Technology companies include Product Companies, Service Providers & Startups; Map Represents only locations with majority Talent Distribution
9,000
8,500
8,000
33,00014,000
9,00012,000
Note: The represented data has been collected from multiple job portals and Draup Proprietary Database updated as of Sep, 2018.
6,100
Amsterdam
Frankfurt
8,100
Sweden
17,000
Dallas/Fort
8,000
Israel
7,000Ireland
7,000
São Paulo
5.600
Madrid Area
12,500 Philippines
3,200
Reading, UK
2,100 Prague
1,900 Helsinki2,000
Cologne
9,000
1,300 Moscow
2,500
7,000Chennai
Greater Boston Area
15,000
8,500Pune
9,000
Poland
Top 10 MSAs
1616
Language Analysis: All enterprises will be ramping up Spanish language services across products and services
Greater Seattle Area
YakimaPortlandSalem
SacramentoStockton
El Centro
Santa RosaVallejo
FresnoSanta Cruz
Merced
Porterville
Modesto
Los Angeles
San FranciscoSan Jose
Salinas BakersfieldSanta Barbara
OxnardOntario
San DiegoTucson El Paso
EdinburgBrownsville
Corpus Christi
San AntoniaHouston
Greater New Orleans Area
MiamiNaples
Fort Myers
North PortSt. Petersburg
Lakeland
Greater Atlanta Area
CharlotteOklahoma City
Greater Chicago Area Greater Detroit Area
ClevelandGreater Boston AreaWorchester
ProvidenceHartford
New HavenBridgeport
AllentownGreater Philadelphia AreaBaltimore
Note: DRAUP’s Talent Simulation Module: The locations mentioned above are major locations with relevant Econometric talent and are based on presence of global as well as local Banking, Financial services and insurance companies. The data is not exhaustive
60-90%
30-60%
0.5-20%
17
Learning Propensity Analysis: Data Science Deep Dive: CMU, MIT and Stanford offer most advanced courses but smaller universities are catching up
Top AI/ Big Data Talent
Universities
Beginner Courses
Intermediate Courses
AdvancedCourses
CARNEGIE MELLON
UNIVERSITY
Cognitive Science
Logic Programming
Machine Learning
Computational Biology
Computer Vision
Neural networksAI &
Manufacturing
UNIVERSITY OF CALIFORNIA
BERKLEY
Decision TheoryProbabilistic-Reasoning
Genetic AlgorithmRobotics
Problem Solving
Neural NetworksIntegrated AI-Architecture
MASSACHUSETTS INSTITUTE
OF TECHNOLOGY
Cognitive Science
Decision Theory
Computational-Biology
Philosophy of AI
Neural Networks
STANFORD UNIVERSITY
NLPMachine Learning
Cognitive ModellingRobotics
Distributed AI
Small Universities/Community Collages Beginner Courses Intermediate Courses Advanced
Courses
THE UNIVERSITY OF TEXAS Speech and Machine Learning
NLPAutomata Theory
Combinatorics and Artificial Intelligence
SONOMA STATE UNIVERSITY Adversarial Game-Tree
SearchFuzzy Logic
Bioinformatics Neural NetworksGenetic Algorithms
POMONA COLLEGE Human Computer InteractionMachine Learning
NLPRobotics
Computer Vision
Biological Problems through Computational Methods
AZUSA PACIFIC UNIVERSITYApplied Machine Learning
Big Data Analytics & Technologies
NLPResearch Psychology
Predicting Chronic Bronchitis Symptoms Using Machine
Learning
WORCESTER STATE UNIVERSITY
Database Design and ApplicationsData Mining
Big Data Analytics Capstone Artificial IntelligenceRobotics
WICHITA STATE UNIVERSITY Artificial IntelligenceRobotics
YOUNGSTOWN STATE UNIVERSITY
Applied Artificial IntelligenceData Warehousing and Data
Mining
Artificial Intelligence in Game Design
Artificial IntelligenceCloud Computing and Big
Data
UNIVERSITY OF SOUTHERN MAINE Machine Learning
Artificial Intelligence and Data Mining
Autonomous Robots
Courses offered by top universities Courses offered by niche universities and Community colleges
Course maturity Low Medium High
Note : DRAUP’s proprietary talent module was used to analyse millennial moments across various cities
1818
Draup is one of the first platforms to arrive at maturity index for talent: Optimal prospects for ML/Data science talent were identified based on suitability of talent availability and maturity index
TelstraAtlassian
Amazon Web Services
IBM Commonwealth BankSuncorp Group
ANZ Bank
Ambiata
QuantiumExpedia
Woolworths Group
IAG ROKT
Westpac Group
Maturity (Years of experience, Cost, projects and skills)
Tota
l Ins
talle
d M
L/D
ata
Scie
nce
Tale
nt
• Commonwealth Bank, Australia is developing ML technology to help with cyber security, fraud detection, regulatory compliance and power big data to use in reducing risk
• Commonwealth Bank has unveiled a chatbot “Ceba” that uses AI to assist customers with tasks such as card activation, checking the account balance, making payments, or getting cardless cash etc.
• Westpac Group is working with start-up Red Marker to use natural language processing techniques to detect content at risk of breaching legal regulations as the content is being created.
• ANZ Bank has been working on Robotic Process Automation to streamline back office work, including helpdesk support and payroll administration.
ML Talent (Availability vs Maturity Index)
Note- Top 15 companies are analysed based of years of experience, cost, key projects and skill types
Entry-Level Talent Pool Mature Talent Pool
Niche Talent PoolLimited Talent Pool
1919
Tracking Micro Certifications: Cisco, Microsoft and VMware are the top companies having partnership with universities in Ireland for certification courses in Networking, Security and Server Management
Major Certifications
University Name Certification CourseCork Institute of Technology CISCO Certified Network AssociateCork Institute of Technology CISCO IT Essentials 1/CompTIA A+Cork Institute of Technology VMware vSphere Fast Track ICM & Optimise and Scale 6.0
Athlone Institute of Technology Microsoft Office Specialist (MOS)Athlone Institute of Technology Cisco Introduction to Networks (CCENT)
Griffith College Ireland Cisco Certified Network Associate (CCNA)College of Computer Training Cisco Certified Network Associate (CCNA)College of Computer Training Microsoft Certified Solutions Associate (MCSA)
Dorset College CompTIA/CISCO A+ Certificate in IT EssentialsDorset College Cisco Certified Network Associate (CCNA)Dorset College Cisco Certified Network Professional (CCNP)Dorset College MCSA - Microsoft Windows Server
Institute of Technology Blanchardstown Certificate in CISCO-CCNAInstitute of Technology Blanchardstown Certificate in CISCO - Network Security 2Institute of Technology Blanchardstown Certificate in PC Maintenance and Networking with CCNA
Institute of Technology Tallaght Cisco Certified Associate Programme
Note : The above information is based on data provided by the respective universities and DRAUP Talent Database
2020
Matching Startups with funding and headcount: Singapore’s ML start-up ecosystem is optimal for acquisition and acquihire, for niche ML/data science skills
Note : DRAUP’s Talent Simulation Module analysed ~1000 start-ups in Singapore to identify top ML based start-ups
Startup Name Key Offerings Company Headcount Total Funding Marquee Customers
Visenze Artificial Intelligence in visualsearch and image recognition 50-100 $ 14 Mn Rakuten, ASOS, Zalora, Carat Lane
CashShield Fraud Detection, Security, Payments
10-50 $ 5.5 Mn Razer, T Mobile, Voyagin, Vodafone
Taiger Automatic Document InformationExtraction through AI 10-50 $ 5.8 Mn Bank of America , Housing and Development Board,
Vodafone, Manulife, NSCS
JobTechArtificial Intelligence and Big Data Analytics start-up that provides
optimized job matching tools10-50 -
Active.ai Enterprise ML Platform for Financial Services
50-100 $ 3.5 Mn
NugitAutomated data analytics,
visualizations, storytelling and sharing
10-50 $ 5.2 Mn Facebook, IBM, Samsung, Audi, NewsCorp, Sanofi
Vi Dimensions Big Data and Machine Learning in surveillance 10-50 $ 1.5 Mn
Tookitaki Machine Learning, Finance, FinTech 10-50 $ 1.23 Mn
BluePool Machine Intelligence, Capital Markets
10-50 -
Jumper.AISocial Media Marketing, Artificial
Intelligence, Information Technology
10-50 - Target, Disney, Volvo, Unilever
2121
Cluster Analysis of job corpus: DRAUP’s analysis of Job postings by 1000 companies estimates the total AI demand to be ~100K
San
Fran
cisc
o Ba
y Ar
ea
New
Yor
k Ci
ty
Bost
on
Seat
tle
Was
hing
ton
D.C.
Chic
ago
Los
Ange
les
Detr
oit
Atla
nta
Dalla
s /Fo
rt
Phila
delp
hia
Denv
er
San
Dieg
o
Aust
in
Min
neap
olis
Data Modelling/Analysis
Machine Learning
Computer Vision
NLP Engineer
Image Recognition
Speech Recognition
Data Scientist
Job Roles Top
MSA
s
<40% 40% to 25% 25% to 15% 15% to 5% <5%
Tota
l Job
Po
stin
gs
40K
20K
7K
5K
3K
4K
9K
LEGEND Cell ColourConcentration of Role in the Location
2222
Optimality of Job Descriptions: Let us look at the recent AI Job description posted by a large Industrial company (Slide1 of 2)
• PhD in Computer Science, Electrical Engineering or related field, with 10+ years of experience in developing, implementing and managing AI/ML related projects
• Strong technical skills on machine learning/AI with proven track record. These technical skills include, but not limited to, regression techniques, neural networks, decision trees, clustering, pattern recognition, probability theory, stochastic systems, Bayesian inference, statistical techniques, deep learning, supervised learning, unsupervised learning
• Experience on developing a long-term analytics innovation strategy and driving those across various levels in the organization
• Strong technical knowledge on big data technologies, cloud, and opensource software tools• Outstanding track record of successful solutions design in the digital space• Demonstrated strong project leadership skills and relevant experience on IoT, connected systems, and
applications• Prior experience in leading innovation teams• A strong track record of starting new activity areas and E2E management of projects related analytics
enabled solutions and transferring to production as appropriate• Development of R&D strategies for new business offerings including market assessment and business case
formulation• Working with industrial partners and /or affiliations, track record in establishing partnerships• Experience in multicultural/global team and influencing decisions at the highest management levels.
2323
Optimality of Job Descriptions: Translating this job description into skills results in over 55 skills (Slide2 of 2)
12+ LEADERSHIP/BEHAVIOURAL SKILLSSTRATEGIC PLANNING
TECHNOLOGY LEADERSHIP
BUSINESS ANALYTICS
PROACTIVE
OPERATIONS PLANNING
BUILD PARTNERSHIPS
ANALYTICAL DECISION MAKING
PROCESS IMPROVEMENT
CRUNCH TIME EXECUTION
EXCELLENT COMMUNICATION
COLLABORATIVE
DECISION MAKING
33+ TECHNICAL SKILLS REQUIREDAPACHE OPEN NLP PROJECT PLANNING NLTK
PREDICTIVE MODELING NEURAL NETWORKS WEKA
STATISTICAL MODELS STOCHASTIC MODELING IMAGE PROCESSING
SAS/SQL DATA WRANGLING OPEN CV
CAFFE TORCH APACHE HIVE
HADOOP SAAS SAS/STAT
SOLUTION DESIGN ANALYTICS TENSORFLOW
APACHE MXNET THEANO KERAS
CNTK SCIKIT-LEARN H2O
SPARK MLLIB APACHE MAHOUT
JAVA SCALA SAS
55+ Technical/Behavioural Skills required for the Entry level Data Scientist Job Role posted by a Large Industrial company
24
24
Location sustainability analysis: Major global hotspots for Software talent will be facing for sustainable water infrastructure challenge
MumbaiPune Chennai
Kuala Lumpur
Taipei CityShanghai
Seoul
Shenyang
ndSydney
Wuhan
Porto
BarcelonaMadridValencia
Sao Carlos
Brisbane
Perth
Bogota
Rio de JaneiroSao Paulo
Santiago
San Jose
Mexico City San AntonioHouston
Miami
Tampa JacksonvilleMemphis
AtlantaRaleigh
Spring Field
TulsaWashington DC
Baltimore
Cairo
Nairobi
Cape Town
Abu Dhabi
Harare
Qatar
Durban
LagosAccra
Lima
Colombo
Belfast
London
EdinburghStockholm
Copenhagen
WarsawBerlin
Gent
Rome
Basel
HangzhouSan Francisco
Seattle
Vancouver
New York
Quebec
Montreal
Analysis of top 150+ global software talent hotspots
Regions in white have abundant supply of water or very low data is available
SOURCES: DRAUP Talent Platform was leveraged to analyse global software talent hotspots | World Resources Institute (WRI) was leveraged to analyse global water consumption
Potential Risk~50% of the water supply is withdrawn , the
cities have good infrastructure
Scarcity Scarcity of water due to low infrastructure
investment or High consumption
Long term risk~25% of the water supply is withdrawn for
Industrial, agricultural and domestic needs.
Approaching danger~75% of the water supply is withdrawn . Low
economic development
2525
Calibrating degree needs: DRAUP Open position analysis for Dallas shows, 27% of the Open positions in IT job roles do not require Bachelors/Masters degree
WHO IS REPLACING THEM?
Lambda School trains students in technology skills and provides them job guarantees, they recently raised
$14M to scale operations across USA
Khan Academy is a platform where experts create content across various
technologies to students to learn and develop expertise
Coursera is also a content based platform which offers certification
based technology courses to students
Holberton school emphasises on project based learning and helps
students improve their technical skills
Note : DRAUP’s proprietary talent module was used to analyse open positions data for Dallas
~27%
9K Jobs analysed
No-degree requirement
Android Developer
Applications Developer/EngineerCloud Architect
Cloud Engineer
Data Architect
Data Center Technician
Desktop Support
Frontend Engineer
Full Stack Engineer
iOS Developer
IT Consultant/Business
Consultant
Technical Support Engineer
Java Developer
Network Administrator
Performance Engineer
QA Test Automation Engineer
Salesforce Developer
Security EngineerSolutions Architect
UX Designer
0.20
0.22
0.24
0.26
0.28
0.30
0.32
0.34
0.36
0.38
0.40
0 50 100 150 200 250 300 350Open Positions
% J
obs
with
Non
-Deg
ree
Req
uire
men
t
Open Positions Analysis - DALLAS
26
26
Niche Professors Analysis: Professors at niche/small universities are driving high level research in AI/ML areas and skilling university students. HR has no easy access to this data.
2
Journal ArticlesTheses
2
Book Chapters
Research Works
Student Profile
Post doc PhD Others
2 8 25
Joe SongProfessor
Department of Computer Science
New Mexico State University
Email: [email protected]
Phone: : +1-575-646-4299
Dr. Song's research lab develops efficient
computational and statistical methods to model
mechanisms of complex biological systems
Research Highlights
Research Goal:
Three-association framework (3AF): (A statistically effective and
computationally efficient algorithmic framework to detect, represent,
and manipulate functional, temporal, and statistical associations
among random variables, to account for causal interactions in
dynamic biological networks)
Reported Topics: q Multivariate likelihood joint quantization;
q Generalized logical networks
q Data-driven discrete and continuous dynamical system modeling
q Data stream clustering
q Computational systems biology applications (Biomass conversion in yeast,
Cell cycle exit modeling in fruit fly, Gene interactions in cerebellar
development)
Software Developed at Lab
Ø ChiNet - Pathway and sub-network rewiring
based on non-parametric discrete models
Ø Ckmeans.1d.dp - A fast dynamic
programming algorithm for optimal univariate
clustering
Ø CPX2 - Comparative chi-square analysis of
interactions. It is implemented as a program for
comparative chi-square analysis of interactions
across different molecular contexts
Ø FunChisq - The functional chi-square test
used by NMSUSongLab and claimed a Best
Performer in DREAM8 Network Inference
Challenges
ØQ-method - Pathway and sub-network
rewiring based on parametric linear and
nonlinear differential equation models
Publications
29 40
Others
Ongoing research initiatives: q Statistical computing
q Computational systems biology
q Neuronal signal analysis
q Computer vision
Specialtiesq Computational Systems Biology; Statistical
Computing; Computer Vision
B.S. in Electrical Engineering at Beijing University of Post and
Telecommunications
M.S. in Electrical Engineering at Beijing University of Post and
Telecommunications
Ph.D. in Electrical Engineering at University of Washington
Education
Student Research Support
Sajal KumarPhD Candidate, New Mexico State University
• FunChisq: Chi-Square and Exact Tests for Model-Free Functional
Dependency
• Simulating noisy, nonparametric, and multivariate discrete patterns
Research Supported / Co-Authored:
27
27
Niche Professors Analysis: Partnering directly with AI professors in smaller universities: AI Professor profiles at East Coast Small Universities/Community Colleges
Professor University Research Focus Areas Citations Contact AffiliationsPatrick McDonaldProfessor of Mathematics
New College of Florida Probability and Stochastic Analysis; PDE; Optimization; Data Visualization;
Artificial Intelligence1622
941-487-4375
Carnegie Learning; European Southern Observatory; Sloan
Fellows
Ankur AgrawalAssociate Professor
Manhattan College Medical Informatics; Data Mining139
718-862-7733
New Jersey Institute of Technology; Manhattan College;
Stanford University; University of California
Amalia RusuAssociate Professor
Fairfield University Human Interactive Proofs; Document Image Analysis;Pattern Recognition; Image Processing; AI; HCI; Web Security
Sunil ShendeAssociate Professor
Rutgers University-Camden
Algorithmic Game Theory; NLP; Parallel and Distributed Computing; Data Compression; Mobile Computing; Data Compression and Encoding; Big Data
[email protected](856) 225-6122
Hewlett-Packard
Roy GeorgeAssociate Professor
Clark Atlanta University Data Mining; Knowledge Management; Information Assurance
and Soft Computing; Artificial intelligence; Machine learning; NA
ASELSAN
Brian RussellAssistant Professor
Rutgers University-Camden
OS, Artificial Intelligence, Networking, Artificial Languages, Software Engineering, and Psychology of Software Development
[email protected](856) 225-6863
NA
Hsin-Chu Chen,Associate Professor
Clark Atlanta University High Performance/Applied Parallel and Scientific Computing; Computer Networking; Algorithm Analysis and Design
NA
Suneeta RamaswamiProfessor
Rutgers University-Camden
Computational Geometry and Applications; Mesh Generation; Computational Statistics; Algorithms; Mesh Generation; Robotics; Computer Graphics.
[email protected](856) 225-6439
AT&T Labs
Scott FreesProfessor
Ramapo College of New Jersey
Web Development; Human-computer interaction; Virtual reality; Software engineering; Database Systems; Computer Graphics
[email protected](201) 684-7726
Sarnoff Corporation
Sourav DuttaAssistant Professor
Ramapo College of New Jersey
Data Structures and Algorithms; Big Data; High-performance Computing; Hardware/software co-design; Machine Learning
[email protected](201) 684-7177
Southern Illinois University Carbondale
Amruth KumarProfessor
Ramapo College of New Jersey
Organization of Programming Languages; Artificial Intelligence; Computer
Graphics; Intelligent Tutoring Systems1494
[email protected](201) 684-7712
NA
John Doucette,AssistantProfessor of CSc
New College of Florida Artificial Intelligence; Multiagent Systems; Machine Learning; Social Choice Theory
[email protected](941) 487-4515
University of Waterloo
David GillmanAssistant Professor
New College of FloridaImage Processing; Health Informatics; Data Science; Artificial Intelligence
[email protected](941) 487-4118
VMware;Akamai Technologies
2828
Career Progression Analytics: DRAUP analysed 24K AI/ML professionals, ~15% of the employed talent has leveraged micro certification courses to enter into AI/ML space
DataScientist
Category Past Role Current Role
Note : The above information is based on data provided by the DRAUP Proprietary Database.
Engineering3-4 years
1-2 years2-3 years
Average Time for transition
Research
Government Administration
Judiciary
1-2 years
2-3 years
1-2 years1-2 years
3-4 years3-4 years3-4 years
4-5 years4-5 years4-5 years
3-4 years3-4 years4-5 years
StatisticianOperations EngineerResearch Assistant
Econometrician
Social Scientist
Research Assistant: Cultural Social Science
Law and Human Rights Researcher
Attorney at lawBusiness Law Attorney
Legal Economist
Behavioural Science ResearcherSocial Science Researcher
Statistics Teaching Assistant
Business/Data Analyst 1
Software Development ExecutiveQA Analyst/Software Testing Engineer
IT AdminData Warehouse Engineer/Database administrator
Algorithms Developer/EngineerWeb/Java Developer
2-3 years6-7 years
1-2 years2-3 years
~85% profiles
~15% profiles
2929
Microhubs is the next generation location strategy where emerging locations with small teams can be leveraged
DRAUP’s AI/ML Talent projections for 2025 estimates emergence of 80 Hotspots, supply is expected to grow to be around 220K
Source: Zinnov Global Machine Learning talent forecasting modelerZinnov analysis of university programs, fresh ML graduates, digital initiatives of Government and Enterprises, StartupsGeneric Data Science Talent pool not considered as there is noise in the data
Pullman
Portland
Eugene Boise
San JoseOrange County
San DiegoLas Cruces
Boulder
Colorado Springs
Kansas City
Lawrence
Lincoln
Ames, Iowa
Iowa city
St. LouisRolla
Fayetteville
Mansfield
San AntonioHouston
New Orleans
Atlanta Columbia
ClemsonKnoxville
CharlotteRaleigh Durham
NorfolkRichmond
FairfaxBaltimore Newark, Delaware
LowellHartfordNewark
Williamsburg
Burlington
Cincinnati
Indianapolis
Tampa St. Petersburg
Santa clara
Hotbeds 2018 Hotbeds 2025
Greenville
Phoenix
Skilled Talent: AI/ML talent installed across US is estimated to be around 220K, 60% of which is installed in top 8 major cities. The Demand for AI/ML talent will reach 2.1M by 2025
University Supply: DRAUP’s Talent module analysed university curriculums introduced in AI/ML areas of over 2K large and small universities and Community colleges across the Tier1/2/3 cities and estimates the rise in supply for AI/ML talent across these 80 cities
Talent Migration Drivers: Higher real estate cost in top east coast locations, Less regulation in the Southern states, Rewiring of the Automotive Industry are major reasons driving the movement of Talent across southern and western cities
San FranciscoDenver
Chicago
Washington DC
New York
Boston
Seattle
Dallas
3030
Talent Cohorts Analysis: An overall talent pool study must consider peer level study to calibrate similar talent
LEGEND Cell ColourConcentration of Role in the Industry
Top EmployersMachine Learning Computer VisionNatural Language
ProcessingData Scientist Data Architect
Computer Software
Information Technology (Services)
Internet
Healthcare/Biotechnology
BFSI
Automotive
Telecommunication
Electronics/Semiconductor
Others
<5% 5% to 15% 15% to 25% 25% to 40% >40%
Note : DRAUP’s proprietary talent module was used to analyse technology talent across different Industries. The Talent count is inclusive on Applied Scientist counts in each of the domains.
3,000 3,500 7,000 5,000 3,500
Microsoft, SAP, IBM, Adobe, Oracle, Intuit,Vmware
Accenture, Wipro, TCS, Cognizant, Infosys, KPIT
Flipkart, Amazon, LinkedIn, OLA, Uber, Google
IQVIA, UHG, GE Healthcare, Philips Healthcare
Goldman Sachs, JP Morgan Chase, Wells Fargo, HSBC, Citi, Morgan Stanley
Mercedes Benz R&D, Bosch, Continental, Ford, Volvo
Ericsson, Nokia, Cisco, British Telecom, Mahindra Comviva, Vodafone
Intel, Texas Instruments, Qualcomm, Applied Materials, NVIDIA, Honeywell, AMD
Fractal Analytics, Mu Sigma, Impact Analytics, GE Global Research, Shell, GE Aviation, Boeing
22KAI/ML Professionals in Bangalore
3131
Recruiter Productivity is impacted due to relying only on key word search in existing profile platforms . DRAUP provides a single platform to analyze data for talent within and outside of organization
How DRAUP can help
• Draup provides an overview of Talent Supply& Demand snapshot and personalized hiringdifficulty/costs of hiring for a given role in agiven location
• Analyze the demographics, diversity, and education background of potential recruits across job roles and locations
• Draup can provide a 360o view of the skillsets required for each Job Role
• Draup generates Skills Maps that help workforce planners to understand the key strengths and capabilities of both external and internal workforce
• Draup provides qualitative indicators and a psychographic profile of each candidate that fits the search criteria
• Example:- Employee Type Tags, Personality Inference, Working Style and Influencers
Updating distributed tracking Systems and
maintaining an employer brand
Crawling Resumes and identifying
relevant/missing Skillsets
Interacting with potential candidates,
conducing pre-hire assessments etc
Recruiting Tools Recruiter Tasks
Skills Mapping
Learning Management Systems
Pre-Hire Assessment
Applicant Tracking Systems
Recruitment CRM
• Bullhorn• Taleo
• Smashfly• Yello
• Hirvue
• Litmos LMS• Talent LMS
• Hirvue• Hundred5• HackerRank• Pymetrics• Quodeit
Workforce Planning
Full-stack View
Soft-skills and Cultural Fit
3232
Catchment Area Analytics: US- Core 5G talent landscape: ~12,000 5G engineers; ~60% of the talent is concentrated at Six 5G catchment areas
ATLANTA
OKLAHOMA CITY
WACO
NEW ORLEANS
HOUSTON
RALEIGH
CHARLOTTE
SAN FRANCISCO
LOS ANGELES, & SAN DIEGO
NEW YORK
BOSTON
WASHINGTON DC
CHICAGO
LAS VEGAS
PHOENIX
KANSAS CITY
SEATTLE
Ann Arbor
Denver
Cornell UniversityYale University
University of California, LA
North-western University
NYU
University of Texas
Purdue University
Boston University
Pennsylvania state university
University of Colorado Boulder
Vanderbilt University
Georgetown University
University of FloridaTexas A&M University
North Carolina State University
Syracuse University
University of Nebraska-Lincoln
MATURE 5G TALENT HOTSPOTS
5G Major Telecom Network Provider presence )
Top universities for 5G talent supply
AT&T
T-Mobile
University of Texas At Dallas
Dish Network
Cox Communications
US Cellular
AT&TAT&T
AT&T
AT&T
Verizon
Verizon
Verizon
Verizon
Catchment Areas
Century Link
Comcast Corporation T - MobileSprint
Sprint Corporation
Potential 5G Markets
CenturyLink
University of South Florida
DALLAS
Charter Communications
~12,000Total 5G R&D Talent across the
country
2k+ Talent33%
22%
20%
17%
9%
Washington DC
Bay Area
New York Area
Dallas Area
San Diego Area
1.4k+ Talent
1.3k+ Talent
1.1k+ Talent
600+ Talent
Note : DRAUP’s proprietary talent module was used to analyse jobs by job roles and skill type across locations
Verizon
AT&T
Core 5G
33
Sociology Researcher Data AnalystDecision Sciences
Researcher
Social ScientistQuantitative Social
Science Researcher
Data and Research Analyst
II
Behavioural Coach Research Assistant Applied Social Scientist
Operational Culture
Social Scientist Research Specialist Data Mining Analyst
Social & Behavioral Sciences
Research Assistant
Graduate Teaching
Assistant - Sociology
Graduate Teaching Associate -
Social Science Statistics
Law and Human Rights
ResearcherSocial Researcher Senior Data Analyst
Data Scientist Job Progression Roadmap
Current RoleInitial Role
DataScientist
Past Role Previous Role
Learning Propensity Analysis: Diverse set of professionals have entered and exceled in the areas of AI/Data Science: Mapping Role Progression Possibilities is a key component of talent pipeline building
~2,000 Data Scientist profile analysed Hilary Dotson
DESIGNATION: Data Scientist at Center For Human Capital Innovation (CHCI)
Education: Ph.D. in Sociology
Specialization: Medical Sociology, Racial Inequality
Research Areas: Machine learning, Statistical
Modelling and Qualitative & Quantitative research.
Role Transition: Started as Research Assistant of
Social and Behavioral Sciences at University of
Central Florida Dotson then moved on to teach
Sociology and also did courses in Exploratory Data
Analysis, R-Programming and Data Science.
Thomas HilbigDESIGNATION: Data Science Researcher at Texifter LLC
Education: Ph.D. in Data Science & Criminology
Specialization: Machine Learning, Crime Statistics
Research Areas: Data Science, Machine Learning,
Crime Statistics and Statistical Computing.
Role Transition: With Degrees in Criminology and
Social Research Thomas explored Data Mining
Techniques for Social Sciences and identified Real-
Time reports using Machine Learning.
Sample Profiles
Note : DRAUP’s proprietary talent module was used to analyse job progressions across data science roles
3434
Organization Structure Analytics: Liberty Mutual : Presence of both horizontally aligned (by HR divisions) & Vertically aligned (Business Line wise) HR teams
Recruitment &
Leadership Hiring
Note : This is an indicative structure based on relevant Job Descriptions & Workloads as mentioned in Job Postings and Draup’s Proprietary Talent Database.
Organization Structure for US Operations
Stephanie TurnerDirector, Diversity &
Inclusion Strategic Programs
Boston (1.5yrs)
Thomas OksanenVP, Employee Benefit
Boston (4.5yrs)
Benefits ManagerBoston
Brenda RuizAsst. Director, Diversity &
InclusionBoston (1yr)
Alejandra VidaurretaDiversity Executive Talent
RecruiterFlorida (0.5yr)
Benefits Diversity and Inclusion
Cara HadleyVP, Sr. Talent Advisor
Boston (11+yrs)
Dennis Goebel VP, Enterprise Talent
Acquisition ProgramsFlorida (9+yrs)
Jodi WallachVP, Talent Acquisition
Connecticut (8+yrs)
Maura QuinnAVP, Campus
Recruiting ProgramsBoston (12+yrs)
Alyson YablonskieSheppeck
Director Talent Acquisiti
onBoston (3+yrs)
April Grogan Director, Recruitment -
Talent AcquisitionKansas (11+yrs)
Lisa Grasso Director Talent Acquisiti
onBoston (0.5yr)
Brian MoorhouseHead , Executive Recruiting
and Enterprise SourcingPhiladelphia (1yr)
Rebecca Virtanen PehSr. Recruiter, Technology
& Emerging SkillsBoston (1.5yrs)
Devony ColeyExecutive Recruiter
Boston (2.5yrs)
Nick PlanteSenior Recruiter
Boston (2.5yrs)
Campus Recruitment Manager
Elizabeth RaymondRecruiting Coordinator
Boston (0.5yr)
Sarah CoderreSr. Benefits Specialist
Boston (3.5yrs)
Tiffany TaylorCampus Recruiter
Houston, TA (6.5yrs)
Years in current organization is mentioned
3535
Amazon
Target
Walmart
Kroger
Hospital Corporation of America
Cardinal Health
National Healthcare Corporation
TrustPoint Hospital
Farmers Insurance Group
Advance Financial
First Tennessee Bank T-Mobile
Ascend Federal Credit UnionConvergyx
Talent Poaching Analytics: Amazon, HCA, Target, Cardinal Health and Walmart are optimal peer employers for desired talent pool in Murfreesboro
Tale
nt S
uppl
y Sc
ale
Recommended Peer Employers
Low Target Peer Employers
Talent Supply vs Talent Affinity
Talent affinity (Attrition rates, Cost effectiveness, Industry relevancy, Training Effort, Growth factors)
• Amazon and Walmart have well trained and large number of telesales talent pool which can be tapped for hiring . Flexible timings, work life balance and regular work shifts are key retention factors for these companies.
• Healthcare providers such as HCA and Cardinal Health have large scale training programmes and huge pay benefits. In spite of these benefits, attrition is expected to be relatively high considering limited growth opportunities.
Customer Support Representative
3636
Platform Component: Ecosystem Insights
Comprehensive data-driven analysis of Peer’s Globalization and talent strategies
Global Work Characteristics- R&D and IT Center Presence- Global Workforce Distribution- Key Programs in all Locations- Leaders Across all Locations (exec movement)
Digital Tech Stack (What tools are they using?- Insight into all the Digital Platforms - Tools and Technologies used by a company
Hiring and Job Opening Analysis- Hiring Trends across location and Sub Verticals- Sub vertical is a deeper element that tracks by
granular subject area
- Insights into key executives which can be hired
Key Skills Hired in last 6 months
Current and Past Job Openings
3737
Platform Component: Ecosystem Insights
Outsourcing Insights- Insight into all the outsourcing partners- Insights around verticals/sub-vertical and
locations leveraged by partners
Executive Movement- list of senior stakeholders who have joined,
exited or been promoted over the past 12 months
Account Compass- insight into the various metrics that are used to
identify an employee’s morale in the organization
Signals- Insight into all company events, investments,
product launches
Comprehensive data-driven analysis of Peer’s Globalization and talent strategies
3838
Platform Component: University Level Insights
Identify Universities with target talent- Identify relevant universities for various
technologies/sub-technologies- Job roles mapping to university curriculums
University Analysis- Curriculum Job roles mapping- Talent supply across various Technologies/job
roles- Curriculum maturity for all technologies-Top employers and Job roles they hire for- Insights around Cost per hire- Top professor profiles
Professor Profiles- Deeper analysis of professor’s expertise- Affiliations analysis
Analysis of Global universities and professor’s expertise, curriculums at technology level
3939
Platform Component: RolodexDiscover potential candidates, predict their organizational fit and strategize how to turn them into hires
Professional Bio- Education- Professional Experience- Biography- Volunteer and Associations
Predictive Attributes to estimate fit- Hiring Fit/ OI- Odds of quitting- Openness to Relocate- Odds of Promotion in Current Role- Job Progression- Skills
Single View of the social graph- LinkedIn- Twitter - Github- Kaggle etc.
Personal InterestsUnderstand Personal Interest & Hobbies to build better relationships and understand candidates beyond just resumes
4040
Platform Component: Personality and culture analysis
Understand personality traits of your potential hires and evaluate fitments with your organization culture
Engagement Guidelines§ Identify engagement drivers and right approach
to engage a candidate
Personal Interests-Identify personal and professional interest areas- Understand drivers beyond just professional Bios
Personality Inference- Understand personality traits and insights
derived on the basis of DRAUP 26 T and Disc Models- Asses Fitment with your organization’s culture and team’s personality vector
4141
DRAUP Delivery model
PLATFORM ACCESS
BRAINDESK
BREAKFAST EVENTS
NEWSLETTER/WEBINARS
Cloud hosted application with intuitive UI, data-rich insights and visualizations
Qualitative insights and primary research led reports about emerging talent trends and deep-dive into specific locations and job roles
Access to on-demand support, custom research and executive-ready presentations
Access to closed door curated networking/working sessions with peers in the talent ecosystem
Biweekly Newsletter and Webinars covering industry leading research around emerging talent trends and shifts