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Machine Learning: Talent Landscape in India

Machine Learning: Talent Landscape in India

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Machine learning, as a subset of AI, has been the buzzword the world over for the past few years. It is also strongly gaining ground in India, which is one of the top 15 countries in terms of machine learning talent, corporates who use ML, and authors who write about it. Cheaper cost of set up and availability of talent has made India a favourable place for tech giants such as Microsoft, Google, IBM and Indian tech players like Tata Consultancy Services to set up their facilities and centres of excellence in this country.

In this report, Rinalytics Advisors, a super speciality executive search firm for analytics talent hiring, presents its findings and insights about the ML talent landscape in India.



Scope:  We try to answer the following in the report:

  • What is the geographical spread of talent in India? Which are the cities they work in?
  • What is the educational spread in the talent pool? What qualifications do they possess?
  • What are their specializations? What is the talent spread between scientific machine learning and applied machine learning?
  • What are their skill precincts?

Definitions:

  • Scientific Machine Learning: Scientific ML is the activity of developing algorithms which enables a computer to independently analyse data.
  • Applied Machine Learning: Practical application of machine learning to solve real world problems.

  • Largest number of ML talent work in the Applied ML space
  • Least number of talent pool work in Scientific ML space
  • Assorted group occupies 15% in the talent pool

  • Most of the machine learning talent pool is spread across 5 cities: Bangalore, Chennai, Hyderabad, Mumbai/Pune belt and Delhi NCR region
  • Highest concentration in Bangalore, and lowest in Mumbai/Pune region.
  • Delhi/NCR has the second highest concentration

  • 51% of the talent pool are doctorates, followed by 37% masters degree holders.
  • Only 12% of the talent pool has bachelor’s as their highest qualification

  • In all three qualifications, number of people in Applied ML is higher than Scientific ML
  • Number of people with Bachelors as highest qualification is least while Doctorates are most

  • We can see that the largest proportion of talent pool is experienced 15 years and above, followed by 10-15 years bucket.
  • The least number of people observed are in the 5-10 years of experience bucket.
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