Forbes India 30 Under 30 2023

How to become a successful Data Science leader

Being an effective leader is not only about making technological choices but also about continuous learning and constantly seeking out new ideas and new methods. Data scientists hoping to become leaders will need to understand that

Published: Jan 25, 2023 04:40:25 PM IST
Updated: Jan 25, 2023 04:52:10 PM IST

How to become a successful Data Science leaderA day-to-day experience in data science requires working on technological aspects such as developing or programming the models for long hours with in-depth research. Image: Shutterstock

Businesses have become increasingly reliant on data, its efficient collection and in-depth analysis to facilitate operations and informed decisions making. The resulting advent of Data Science in the corporate sector has generated significant demand for data scientists, both technical and leaders. Data Science is a thriving field with a remarkable number of job openings around the globe. The demand is overflowing the supply.

Data Science is one of the most lucrative jobs of the 21st century but like all other jobs, this requires hard work. A day-to-day experience in data science requires working on technological aspects such as developing or programming the models for long hours with in-depth research. When we are aiming for a leadership role, your experience needs to shine.  

For practitioners, leadership means managing people and products with a focus on developing workflows and practices that will help companies succeed. But data science requires navigating the fast-changing demand alongside peers and stakeholders across multiple disciplines. Leading a team is a dream for many of us. It’s the natural progression along the career path, and everyone has ambitions to grow in their career, to make a mark on their company, clients, and in the analytical world.

But leading is tough. There is nothing that adequately prepares you to lead an analytical team. On top of this, data science needs project management and leadership styles to drift into comfortable territory. In the data science domain, we must learn cutting-edge technologies and need to master the art of leadership. Thus, the management philosophies that work well in the rest of the organisations may not translate well to data science teams.

Businesses don’t know what to do with their data. It’s a new world with the potential to derail even the most earnest data science initiatives. Therefore, as a data science leader, you need to bridge the gap between the different business stakeholders and the data scientist team. Be in continuous communication with business stakeholders to help them identify the business problem. Help them convert the business problem into a data problem. Work with the data science team to formulate a hypothesis, data collection or extraction, data modelling and development and be able to communicate the results and insights to businesses and help them design and develop their strategies.

Also read: Data matters: 4 workplace functions


How can you transform yourself as a Data Science leader and excel in the role? Data Science is a multi-faced role. With your experience and background as a leader, you bring a lot of experience to the table. But you need to enhance a few skills such as problem formulation ability, storytelling and communication, exposure to end-to-end machine learning algorithms, and structured thinking.

As a leader, you need to hone your people skills. You will be less responsible for algorithms and model development but more in tune with building the data science team. Focus to set the right objectives of the data team ahead and define the business objectives; define the organisational structure for the team and hire the right set of technical capabilities in a team: Data Engineering, Data Analytics, and Data Science, to name a few.

Data science and business analytics are cross-functional domains and the data team’s core mission is to help the business extract maximum value from data and become data-driven. Therefore, proximity to the business is indispensable. So while hiring, along with technical capabilities, one needs to focus on communication, business knowledge, ethics and security mindset, flexibility, and adaptability. Ethics play a vital role in data science as in the early stages, the processes are vulnerable, thus the right hire will ensure best practices are implemented to build a foundation for data protection and governance.

Also read: How AI will democratise strategy for the next industrial revolution

Being an effective leader is not only about making technological choices but also about continuous learning, and constantly seeking out new ideas and new methods. Afraid of all the challenges that are supposed to come your way?

Well, not anymore. To make a consistent, meaningful impact as a data science leader, you need to learn to articulate technology roadmaps, plan effective project strategies, support diversity, and create a positive environment for professional growth. Do not stop your data science learning process; data science is fast evolving. Continuing your learning in this domain is the key to being successful at growing yourself and your team’s skill set.

Dr Sshruti Mantri is the Associate Director at ISB Institute of Data Science (IIDS)

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[This article has been reproduced with permission from ISBInsight, the research publication of the Indian School of Business, India]

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