key learnings on how they set up the data teams at TheMathCompany.

1. Story of Scaling Data Science Hiring
Proprietary and Confidential

2. ▪ Leading Analytics Transformation for the world’s largest beverage company by
designing and operationalizing an Analytics COE and training the business on how to
drive decisions through Analytics
▪ Worked with a major US-based Airline in improving the OTP ranking many-fold
▪ Built small and large analytics teams grounds up, developed talent, processes and
culture and grew them into multi-million dollar accounts
▪ Created and implemented “Rapid Impact Analytics” strategies across clientele to reduce
time lag between problem origin, identification, decision and action
Srinidhi Rao, Head of Delivery, TheMathCompany
Education
MBA
Kelley School of Business, Indiana University, USA
B Tech
NITK Surathkal, India
Industries
Retail, eCommerce, CPG, Airlines,
Marketplaces
▪ Heading Solutions & Customer Satisfaction at The Math Company
▪ Over 15 years of experience in Advanced Analytics, Management Consulting,
Manufacturing and Software Development
▪ Enabled setting up analytics centers of excellence in India for 3 Fortune 500 companies
▪ Established recruitment, training, project management and knowledge management
processes to run analytics at scale, which later became organization standards
▪ Reputed thought leader, author and regular speaker at key industry events
Experience
Key Achievements
Proprietary and Confidential

3. Proprietary and Confidential3
OUR MISSION
“Enabling viable and valuable
data & analytics
transformations”

4. Proprietary and Confidential4
WHY IS THIS TOPIC IMPORTANT TODAY?
▪ Every company wants Data Scientists that have all relevant and
peripheral skills at expert level
▪ Recruiters are being less than successful in satisfying this need, wrt
quality of Data Scientists and speed of hiring
▪ Most “expert” Data Scientists are under-employed leading to attrition
and further pressure on hiring
▪ YOU being at the centre of this action can change it for the better

5. Proprietary and Confidential5
How do we solve this problem?

6. Statistics/Mathematics
Statistical Programming (Ex:R,Python,etc)
Business Context
Technological Skills
(Ex: Hadoop,HIVE,etc)
Communication Skills.
Problem Solving
• Analytical Thinking.
• Consulting Skills.
RECAP OF SKILLS

7. Proprietary and Confidential7
OUR LARGE SCALE EXPERIMENT
▪ Hire high potential youngsters and train them a little bit on everything
▪ With time, they become great generalists who understand all aspects
of Analytics

8. Skill sets required Our Experiment
Statistics/Mathematics
Statistical Programming
(Ex:R,Python,etc)
Business Context
Technological Skills
(Ex: Hadoop,HIVE,etc)
Communication Skills.
Problem Solving
1. Analytical Thinking.
2. Consulting Skills.

9. Proprietary and Confidential9
WHY COULD IT NOT BE SUSTAINED?
▪ As the years progressed, the field matured, requiring specialists in each
field
▪ The maturity curves of each skill takes a different path and for which
the generalists don’t get opportunities
▪ Eventually, good generalists become great generalists, but not
specialists

10. Proprietary and Confidential10
So, How do we solve this problem?

11. 11
SEPARATE BI/DASHBOARDING FROM DATA SCIENCE ROLES
Source 1
SQL
Dashboard Visualization
48%
10%
20%
35%
Source 2
Source 2

12. 12
HAVE CLARITY ON DS VS DE VS BA ROLE
Business
Business analysist/
Project manager
Data Scientist
Data Engineer
Visual
Logic/Stat
DB layer

13. Proprietary and Confidential13
HOW DO WE MAKE THIS HAPPEN?
Ask the 3 fundamental questions
1. Which organization in the company is the role going to be in?
2. What is the nature of projects that will be executed? What
technologies does the organization use?
3. What is going to be the role of the new hire?

14. Proprietary and Confidential14
LAST BUT NOT THE LEAST…
Collaborate with the Hiring Manager!