Ishu Jain is the Head of Central Analytics at Swiggy. She has over 16 years of diverse work experience in delivering analytical insights in companies like Dell, Genpact, Fractal, Target and Swiggy for business entities across geographies. Over the years, she has evolved as a domain agnostic analyst, trusted Leader and a constant learner.

Can you tell us about your AI journey and what made you interested in AI?   

My professional journey started during college. I met someone who had done Masters of Business Economics from Delhi University back in 2004 and was very well placed. She could not get through the Delhi School of economics as only 150 students got into it across the country. She explained to me this new discipline which would become prominent in India in the coming years. I somehow latched on to the idea and did my Honors in Economics and statistics from Sriram College of commerce and then Master's from Delhi School of Economics. 

There was no looking back after that as I got a place as a rookie data scientist in Genpact, then worked in Dell up to manager deploying in production ML models across geographies, setting up a vendor ecosystem etc. After that, I moved to Fractal, where I also learnt how to manage a P&L working in analytics consulting, growing an account almost 4-fold in 2.5 years, learning a lot in terms of client management, escalation management, change direction, setting up processes for a large-scale performance in terms of people etc. I am able to leverage all these learnings at Swiggy as the head of analytics for all the central functions with all this learning. I started with Delivery optimization and then expanded my scope gradually. It was initially tough as I did not have much idea about consumer tech; however, it's been almost three years now, and I am thoroughly enjoying my stint here. To see the impact of your work in real-time is the most satisfying feeling

What is your area of expertise in AI, and what made you choose it?   

As a discipline, I have specialized in ML algorithms – Supervised, unsupervised, forecasting, recommendation system etc. I had done my dissertation on forecasting the stock markets, hence got interested in that. At work, more than the technique, I find the process very interesting. Seeing insights from the data to do feature Engineering, looking at results, debugging – I enjoy all of it very much  

Can you say something about your current role and your handling projects?   

Leading team of 100+ data scientists/ analysts across central functions- delivery network optimization including geospatial analytics, Fleet Assignment and efficiency, payout structure, Demand and supply and optimization of the actual state of Swiggy App (storefront) for growth, customer segmentation, loyalty, traffic and acquisition. The key responsibilities are being the escalation point for all the requirements from product & Engg involving all aspects of operations & delivery – influencing product roadmap, resourcing, planning, deliverable quality, productivity, innovation and change management to meet company goals for business impact. 

Can you describe some challenges you have faced in reaching where you are now?  

There were several challenges in the journey, starting from –   

• Being too technical to realize the importance of business impact. This itself needed a shift in thinking and developing skills like business communication, speed and action-oriented solutions to ensure business problems are solved in a timely manager generating the right impact  
• Motherhood – Usual problem with any woman, only that I did not have support from the families, maternity leave was 84 days, and I left my three-month-old baby with a Nanny at home all alone and went to work. I never thought that my career was optional and hence did whatever best I could do given the circumstances. This does mean that you need to take a back seat for a few years, but again I don't think I did that. I worked hard on myself to ensure my career always progressed in the right direction  
• I have also had a very steep learning curve in the last few years, from the tech giant to consulting to a start-up. It burns you out on some days. To make your place in all kinds of environments/domains etc. is highly challenging. One needs to find a new depth every time.

How is Swiggy using AI to improve its services?   

Swiggy use AI everywhere to improve its service – Tip options and discount coupons provided to each customer, customer segmentation, simulating payments for drivers, supply forecasting, recommendation of restaurants, experimentation etc.   

Do you see enough female representation in tech roles within start-ups? What kind of difference would it make to instate women in tech roles?  

There is a very dismal representation of females in tech roles in tech start-ups. I believe it's the difficult working environment – long hours, the expectation to be available all the time, weekends, etc. This makes it extremely difficult for married women to survive, especially in mid-senior roles. At junior levels, I do not see that difference in representation. Women as leaders bring in different kinds of energy/thought processes to the teams – more structure, better planning, tighter management, do more with less, multi-tasking and empathy. Over time it will increase the team's impact multifold in any organization. Start-ups, however, take pride in chaos and bias for action etc., ignoring the other aspects which become essential as they continue on that path.   

Discrimination by AI is a genuine concern while ensuring data integrity - does this exist? If so, how can it be addressed?   

Discrimination exists but subconsciously to meet the fast-paced growth targets for start-ups. It does not exist consciously as malpractice, e.g., promoting specific payment instruments for customers to improve customer experience and hence get more orders. Here the idea is both experience and growth and not realizing that in the process, we may end up promoting only certain giants as they are used at a mass scale already. It can be addressed by having a conscious forum and active participation to understand the pros and cons of these clearly. These issues should be debated from all angles and all domains across industries. Only clear understanding and consensus can bring this change apart from policy/regulations  

What do you want to say to women who want to build a career in AI and other tech-related fields?  

All the women who want to bring a career in AI and tech-related fields are working hard and equal. However, the mindset needs to change in the overall approach to work. Women have the skill required to be at the top or should ideally acquire the quant plus coding skills needed. The demands of such a career are different. It will be critical to constantly upskill, find the right mentor, network with the right people, know the trends, and actively think about them.