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Council Post: The Great Attrition In Data Science — What Do Experts Say?

The Great Attrition In Data Science — What Do Experts Say?

“The Great Resignation” is picking up again – attrition in the Indian data science space is possibly at its highest so far. The country’s generally significant rate had almost halved amidst the pandemic, owing to the fear of no new opportunities. Now that hiring sprees are back, so are potential attrition instances. The rates are predicted to shoot up from 12% last year to 23 by the end of 2021, adding up to one million potential resignations. 

The industry is massively imbalanced, with a high demand for data scientists and a low supply of capable experts. This is a matter of concern for the Indian IT industry and data science organisations banking on data scientists. The industry leaders at AIM’s Leaders Council have created and/or led some of the most sought after companies to work at by data science professionals. Today, they have leveraged their years of data science experience to share inputs on why the attrition rates are so high and suggest successful ways to retain the best talent. 

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Why are attrition rates so high?

  1. Lack of Connection between Employees And Employer

In the “work from home” mode, the relation between employees and employers has become very transactional. There is no sense of belonging, no connection to a bigger purpose or the sense of a team accomplishing something together. We often fail to realise that when one spends a good amount of their awake time in the office, it is not just about the work but also about the social connection they build with their colleagues. We need to focus on engaging more with our team members, creating a sense of belonging and helping them connect to the organisation’s bigger purpose.

Sayandeb Banerjee, Co-Founder and CEO at TheMathCompany
  1. Move to Start-ups

There have been more opportunities, especially in the start-up ecosystem. That, along with a fear of missing out (FOMO) when their colleague gets an offer, have been some of the key reasons for the high attrition rates. 

Manoj Madhusudanan- Head of dunnhumby India
  1. High Salary Expectations

Given the market situation, folks across entry-level to 5-6 years experience expect a hyper-growth in salary, designation, roles and responsibilities. While this is fine in cases where it is deserved, it becomes tough in a services scenario where a higher salary or designation comes with the expectations on other attributes like solution strategy, data science thought leadership or client management skills as against just technical depth and execution, which a lot of purely technical focussed folks are not able to demonstrate neither are keen to ramp up on. Such expectations lead to dissatisfaction on both employee and employer end, misalignment in organisational vs personal goals and assessment against those, slower growth and salary hikes compared to captive units, among other challenges. 

Ruble Joseph, Lead Strategist (VP) – Global Data Science and Analytics Practice
  1. Inability to be Generalists 

When employees join the company while we are already working on a project, there is a mismatch between candidates expectations vs what they end up working on, at least at the start of the tenure. This becomes challenging with folks with a myopic view of working on specific algorithms, platforms or solution modules. These folks usually leave as they are not open to playing a generalist data scientist role without realising the broader set of areas, roles, opportunities, including innovation and first of its kind solution development that they may get in the future once they spend some time with the organisation

Ruble Joseph, Lead Strategist (VP) – Global Data Science and Analytics Practice

While the leaders have agreed that attrition rates are high, according to Manoj Madhusudanan, recent trends indicate that the frenzy has already peaked. But what can leaders do to ensure they have a low attrition rate and high employee satisfaction? 

How can this be tackled to retain the best talent?

  1. Role-specific Hiring

The trick is having detailed job descriptions and hyper-customised role-specific hiring. This, along with differentiated persona roles for generalists vs SMEs, customised career tracks, expertise area-specific roles, can make a big difference in employee retention. Support these specific hirings with better alignment, customised performance evaluation parameters and goals and allow for the ease of shuffling across roles. 

Ruble Joseph, Lead Strategist (VP) – Global Data Science and Analytics Practice 
  1. Campus Hiring & Grooming from the Get-Go

Creating and grooming talent as against only lateral hiring. Folks who start their data science journey with a particular organisation tend to stick longer than laterals hired at higher salaries if given adequate opportunities to grow fast. Create a smaller cluster of teams with the opportunity for multiple folks to step up and grow faster, take ownership, accountability and create leadership visibility much faster. 

Ruble Joseph, Lead Strategist (VP) – Global Data Science and Analytics Practice 

I believe we should hire more from campuses at entry-level and groom them intensively, rather than simply hiring trained people from each other. We have seen that those hired from campuses grow with the company and are less likely to leave. 

Manoj Madhusudanan- Head of dunnhumby India
  1. In-house Learning and Upskilling Opportunities 

It’s imperative to ascertain that professionals are continuously growing, working on a variety of assignments. Lack of growth in skills or boredom becomes a trigger for looking out.

Swati Jain, Vice President Analytics at EXL Service

Invest in programs to upskill citizen data scientists and create a substantial backlog of internal talent to augment your current data science needs.

Ashwin Thota, Principal Data Scientist at Bose
  1. Offering Multidimensional Flexibility

Most data scientists are high achievers; they want to be constantly challenged with new opportunities. Consider giving them “freedom within a framework”. Allow them to take some learning time to explore new ways of solving existing business problems with data.

Ashwin Thota, Principal Data Scientist at Bose
  1. Rewards Go a long Way

Most data science roles inherently include a research component. Reward your data scientists for ANY finding they bring to the table. Realise that there are no bad results from a well-conducted experiment.

Ashwin Thota, Principal Data Scientist, Bose
  1. Establishing a Link between the Role and the Impact

While Great Resignation has led to job changes across roles, I believe that one thing that results in Data Science attrition is the kind of work handed over to them and the impact. 

Nalin Goel, Senior Vice President – Product at MoEngage

I believe in Nitin Nohria’s ABCD model for workplace motivation drives: Acquire – Tangible benefits; Bond – Connection, friendship; Comprehend – Meaningful work, skilling; Defend – Recognition. “Comprehend” plays a significant role in the analytics and data science space. Understanding the expectations of the role and connecting the same to the desired output is key in growing an inbuilt sense of motivation for the job.

Satyamoy Chatterjee, Executive Vice President at Analyttica Datalab

In the fast-flowing resource that is Data Science, attrition is a hole in the pipe, carrying a humongous potential to slow, if not bring the system down. The pieces of advice from our council leaders provide us with both the visible and invisible causes behind the situation and, at the same time, give us an insight into solutions that work. 

India is home to cities setting examples in the fast-paced technological boom. As per statistical research by London & Partners, Bangalore emerged as the world’s fastest-growing technological hub since 2016, overtaking London in the first position, followed by Mumbai at the sixth. Seeing this, it becomes worrisome to observe that attrition remains higher in the Indian IT industry than in other countries of the world. Clearly, data science organisations and leaders need to make retention a focal point in the employee lifecycle to ensure the constructive growth of not just the organisations but the industry as a whole.

This article is a collation of quotes by members of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill the form here.

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Avi Gopani
Avi Gopani is a technology journalist that seeks to analyse industry trends and developments from an interdisciplinary perspective at Analytics India Magazine. Her articles chronicle cultural, political and social stories that are curated with a focus on the evolving technologies of artificial intelligence and data analytics.

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