TC Interview: Jaya Kolhatkar, VP of Data, Walmart Labs

Data scientist Jaya Kolhatkar joined Walmart when the retail giant acquired her company in 2013. 

She talked to TC about predictive analytics, data science and diversity and about how being a mentor is an important part of her role as VP of Data at GeC, Walmart Labs.

1. What is your role at Walmart?

I lead the data team at Walmart Labs. The data team is basically responsible for creating all of the data infrastructure and analytics that our data scientists and business analysts leverage to understand what’s going on in the business through dashboards and business intelligence; for our data scientists it’s a place to build and deploy algorithms.

I also own the predictive analytics platform. This is the company with which I came to Walmart. That platform allows us to do a lot of the real time predictive algorithms within the site. So my particular role is to make sure that the teams build and deploy algorithms that will help us move into the future at a much faster pace.
 

2. You were an entrepreneur before joining Walmart. How did that happen?

I did not start off as an entrepreneur but later in my career I decided that was a good thing for me to think about. Predictive analytics has always been the purview of larger companies, which have the resources to run and fund them. So my thought was to build a platform that would make this available to smaller companies as well – where it would be easy for them to run predictive analytics themselves.

So a group of colleagues got together and started our company in 2011. We had a really great time with it – a lot of interesting companies tested and deployed on our platform, and as we were talking to Walmart about becoming a customer of ours, they decided to buy us!

When we started thinking about it, we could not imagine any other way to get to the scale that we’d always hoped for our platform – but going through Walmart – that was our fastest and easiest path.
I can tell that today, two and a half years after we were acquired, our platform takes all of Walmart’s e-commerce as well as Walmart stores’ transactions, and I don’t know if we’d have gotten that scale if we had travelled that path on our own.
 

3. What are your views on being a woman and a minority in tech and engineering?

Clearly, Walmart does a lot of work making sure we are a diverse organization. But it’s not just diversity from a gender perspective – it’s a diversity of thought, it’s the diversity of talent that we bring in. You want to bring in folks that come in with very different points of view and very different experiences of life so that when they start solving a problem, you have a 360 view of the problem - and you can solve it much better.

Now I’m very passionate about women in technology especially in the retail side. Most of our customers are women. And it’s awesome to know that you are building something that is going to affect the lives for the better, of women who are shopping at the site.
 

4. Why does a data scientist need to think about business strategy?

I think a data scientist can only achieve their fullest potential if they can understand why we are using algorithms and data. It is critical for them to step back and understand what the business is about. What is the strategy? Where are we pointing? They have to act as product managers.

A really awesome data scientist is actually a product manager who’s thinking about the data product they are going to embed in a business process and how that will allow them to get the results they are looking for.
It’s really important for them to know they are building algorithms for a purpose and not being just a research exercise.
 

5. Looking back at your professional development, can you tell us how you managed the transitions you made in your career?

I was very lucky when I first started. I worked for a gentleman who was into predictive analytics. He made it his business to make sure I learned how to think about being a data scientist –it wasn’t called data science then; that’s a new term now – we were statisticians, not data scientists but we did similar things. We did code, it may not always have been in C+ or Java, but it might have been in a statistical language. We wrote algorithms and we acted as product managers because there were areas that we were responsible for.

It took me probably three or four years before I felt I like I was competent enough to think about a solution holistically and say, ‘I think I get it, I know what your needs are and I can devise an algorithm or I can devise what data I should leverage to solve that problem.’

It takes a little while for you to get used to understanding how your work relates to the business.
 

6. Did you have a mentor and how did that help guide your career?

Mentorship is unbelievably important. Without this first boss of mine, I don’t think I would have been in this field, and I don’t think I would have been as successful.
 
Later on in my career a lot of my mentors were folks that I cultivated more for them to help me understand things I was not seeing myself.

It wasn’t a lot about how to move from Point A to Point B, but it was more about things you’re blind to. And that’s what was very helpful. It was also helpful to help me figure out the tentativeness I had about taking on a new role – you know women tend to want to be perfect before taking on a new role. And, I was taught fairly early on in my career that if you knew 40% of the role, not to really overthink it but to take a leap of faith.
 
I had people that pushed me quite a bit, early on in my career, to take on opportunities that I probably wouldn’t have done myself.
 

7. Do you mentor people in your organization?

I do a lot of work with mentorship. Walmart is very active in making sure that is part and parcel of life. At Walmart all those at and above Director level have some portion of their bonuses tied to mentorship and diversity. And so we each have two mentees.

I also run a mentor circle, which is for senior women in the organization, where we talk about areas from which we can all benefit. It’s about bringing together issues that our mentees might have.

Talking Cranes

Data scientist Jaya Kolhatkar joined Walmart when the retail giant acquired her company in 2013. 

She talked to TC about predictive analytics, data science and diversity and about how being a mentor is an important part of her role as VP of Data at GeC, Walmart Labs.

1. What is your role at Walmart?

I lead the data team at Walmart Labs. The data team is basically responsible for creating all of the data infrastructure and analytics that our data scientists and business analysts leverage to understand what’s going on in the business through dashboards and business intelligence; for our data scientists it’s a place to build and deploy algorithms.

I also own the predictive analytics platform. This is the company with which I came to Walmart. That platform allows us to do a lot of the real time predictive algorithms within the site. So my particular role is to make sure that the teams build and deploy algorithms that will help us move into the future at a much faster pace.
 

2. You were an entrepreneur before joining Walmart. How did that happen?

I did not start off as an entrepreneur but later in my career I decided that was a good thing for me to think about. Predictive analytics has always been the purview of larger companies, which have the resources to run and fund them. So my thought was to build a platform that would make this available to smaller companies as well – where it would be easy for them to run predictive analytics themselves.

So a group of colleagues got together and started our company in 2011. We had a really great time with it – a lot of interesting companies tested and deployed on our platform, and as we were talking to Walmart about becoming a customer of ours, they decided to buy us!

When we started thinking about it, we could not imagine any other way to get to the scale that we’d always hoped for our platform – but going through Walmart – that was our fastest and easiest path.
I can tell that today, two and a half years after we were acquired, our platform takes all of Walmart’s e-commerce as well as Walmart stores’ transactions, and I don’t know if we’d have gotten that scale if we had travelled that path on our own.
 

3. What are your views on being a woman and a minority in tech and engineering?

Clearly, Walmart does a lot of work making sure we are a diverse organization. But it’s not just diversity from a gender perspective – it’s a diversity of thought, it’s the diversity of talent that we bring in. You want to bring in folks that come in with very different points of view and very different experiences of life so that when they start solving a problem, you have a 360 view of the problem - and you can solve it much better.

Now I’m very passionate about women in technology especially in the retail side. Most of our customers are women. And it’s awesome to know that you are building something that is going to affect the lives for the better, of women who are shopping at the site.
 

4. Why does a data scientist need to think about business strategy?

I think a data scientist can only achieve their fullest potential if they can understand why we are using algorithms and data. It is critical for them to step back and understand what the business is about. What is the strategy? Where are we pointing? They have to act as product managers.

A really awesome data scientist is actually a product manager who’s thinking about the data product they are going to embed in a business process and how that will allow them to get the results they are looking for.
It’s really important for them to know they are building algorithms for a purpose and not being just a research exercise.
 

5. Looking back at your professional development, can you tell us how you managed the transitions you made in your career?

I was very lucky when I first started. I worked for a gentleman who was into predictive analytics. He made it his business to make sure I learned how to think about being a data scientist –it wasn’t called data science then; that’s a new term now – we were statisticians, not data scientists but we did similar things. We did code, it may not always have been in C+ or Java, but it might have been in a statistical language. We wrote algorithms and we acted as product managers because there were areas that we were responsible for.

It took me probably three or four years before I felt I like I was competent enough to think about a solution holistically and say, ‘I think I get it, I know what your needs are and I can devise an algorithm or I can devise what data I should leverage to solve that problem.’

It takes a little while for you to get used to understanding how your work relates to the business.
 

6. Did you have a mentor and how did that help guide your career?

Mentorship is unbelievably important. Without this first boss of mine, I don’t think I would have been in this field, and I don’t think I would have been as successful.
 
Later on in my career a lot of my mentors were folks that I cultivated more for them to help me understand things I was not seeing myself.

It wasn’t a lot about how to move from Point A to Point B, but it was more about things you’re blind to. And that’s what was very helpful. It was also helpful to help me figure out the tentativeness I had about taking on a new role – you know women tend to want to be perfect before taking on a new role. And, I was taught fairly early on in my career that if you knew 40% of the role, not to really overthink it but to take a leap of faith.
 
I had people that pushed me quite a bit, early on in my career, to take on opportunities that I probably wouldn’t have done myself.
 

7. Do you mentor people in your organization?

I do a lot of work with mentorship. Walmart is very active in making sure that is part and parcel of life. At Walmart all those at and above Director level have some portion of their bonuses tied to mentorship and diversity. And so we each have two mentees.

I also run a mentor circle, which is for senior women in the organization, where we talk about areas from which we can all benefit. It’s about bringing together issues that our mentees might have.

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