Natalia Lyarskaya PhD, Chief Data Officer at financial services company ZestMoney, shares her top achievements of 2021, her plans for 2022 and her advice for aspiring data leaders
What would you say have been your key achievements at ZestMoney over the past 12-24 months?
The first one is the complete restructuring of our data science capabilities. They were previously coupled with the credit risk strategy vertical because, initially, the key use case for applying data science was the data- and algorithms-driven credit underwriting process (i.e. how we onboard new customers and how we take decisions about their credit line, interest rates, etc).
With the business scaling and growing, we realized that it’s the right time to apply data science and AI capabilities outside just credit strategy. We’ve been doing a lot of POCs [proof-of-concepts] with the collection, the customer support, the marketing and the product teams. So, we decided to separate from the credit strategy team completely and create a dedicated, centralized data science and AI capability.
Together with this, the fact that many POCs that our data scientists have been working on have successfully transformed into live functionalities and features means this was also the year when stakeholders across multiple functions realized and could actually see the value that data science and machine learning capabilities could deliver to them.
How would you say ZestMoney’s data maturity evolved in 2021?
We’re now building certain systems at scale, where we go beyond just a POC stage, and are doing a lot of experiments, A/B testing and moving towards scalable and agile solutions.
The next step for us is doing that, not on a case-by-case basis, but actually having a proper infrastructure in place by creating a fully functional MLOps system to deliver AI solutions faster and in a more robust and controlled manner.
What advice might you give to aspiring data leaders, in terms of what characteristics or skills they should focus on developing?
A few things come to mind. The first one is, never stop learning. This probably applies to everyone, but even more so to a data analytics leader, especially to the ones working closely with the technology.
The pace of new technology development is accelerating, emerging business models are scaling up and the social-political landscape is moving constantly. One should be open to learning and trying new things in order to be successful. But not just the hard skills around technology, coding languages, etc, but also the adaptability that one needs inside themself to help adjust to change. [I mean] ‘adjust’, not in a passive sense, but leveraging this change; embracing it. The saying goes, ‘In times of change, learners inherit the Earth while the learned find themselves beautifully equipped for a world that doesn’t exist.’ And this always holds true for me.
Secondly, what makes data and analytics leaders successful is their ability to evangelize usage of data, algorithms, analytical tools, best practices across the whole business, and even externally. And this is where openness and the ability to listen are needed. You do not want to impose things, but to be perceived as someone who is able to put themself in the shoes of a counterparty, understand the problems of the rest of the business and find the right and most efficient solutions for them.
What are the key things you want to achieve in 2022?
As a business, we are so much looking forward to 2022 to continue our growth, making it stronger and even more sustainable. We’d also like to establish ourselves as data and AI leaders in the financial technology industry. This goal is directly translated into what I’d like to achieve in 2022.
First, I want to scale the solutions we are currently building and reach a good level of maturity in terms of scalability and performance. I’m specifically talking about the AI and ML models and tools we have built. It’s time to further enhance them, to optimize the whole infrastructure in for MLOps, to do more of A/B testing at scale. From a strategy execution point of view, this is the key focus.
My second priority is to start applying AI and predictive analytics to more and more use cases. There is a lot of attention in the business around retaining our existing customers, increasing their engagement with products [and] increasing their lifetime value, and this comes down a lot to building the personalized experience across the whole customer’s lifecycle (from onboarding to repayment and cross-sell), optimizing our recommendation engine and generating customer-specific content. In the new year, I believe this is where data science team can deliver the highest value and ROI.