Bridging Business and AI: An Interview with Hatim Kagalwala, Data Scientist Driving Customer Insights through Machine Learning

12 May,2025 05:30 PM IST |  Mumbai  | 

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Hatim Kagalwala


In today's world of rapid digital transformation, few individuals embody the intersection of data science, innovation, and real-world business impact quite like Hatim Kagalwala. With a career that spans financial giants like American Express to tech leaders like Amazon, Hatim has built a reputation for turning complex data into actionable insights about customer behaviors and preferences, driving growth, innovation, and greater economic inclusion.

Behind the technical skills and academic accolades lies a professional deeply motivated by purpose-using data not merely to generate insights, but to drive meaningful and scalable change.

In a recent interview, Hatim shared details about his journey - from his beginnings in mechanical engineering in Mumbai to leading machine learning initiatives at some of the world's largest companies. Here are some excerpts from the interview:

Interviewer: Hatim, can you take us back to the beginning? How did your journey into data science and applied analytics start?

Hatim: Absolutely. My Bachelor's from the University of Mumbai focused on mechanical engineering. During this time, I discovered my interest in mathematics and problem-solving, becoming particularly fascinated by how models could explain or predict real-world outcomes. That curiosity led me to pursue a Master's in Financial Engineering at NYU's Tandon School of Engineering. During my master's program, I took a course called 'Machine Learning in Finance,' which introduced me to the transformative potential of machine learning and sparked my interest in exploring the endless opportunities within data science, statistical modeling, and predictive analytics. Combining finance, technology, and data was a natural progression for me, and that's where the journey truly began.

Interviewer: You've worked across industries - from fintech startups to Amazon's global teams. How has your approach to data science evolved over the years?

Hatim: When I started at Credibility Capital, a fintech lending platform, the focus was very tactical - building logistic regression models, optimizing lead generation pipelines, improving default prediction accuracy. It was hands-on and very rewarding. This was the kind of experience I needed that prepared me for a larger scale at American Express.At American Express, I was developing time-series forecasting models for global credit portfolios, influencing investment decisions, and ensuring financial stress testing was robust - especially critical during uncertain macroeconomic conditions. Now at Amazon, the environment is even more dynamic. I focus heavily on machine learning at scale, working on demand forecasting, causal inference modeling, credit product innovations, and seller enablement. Through all of these experiences, one thing stayed constant: Data science isn't just about algorithms. It's about solving business problems reliably and responsibly.

Interviewer: Speaking of Amazon, can you share a few projects you're most proud of during your time there?

Hatim: One of my favorite projects involved leveraging customer purchasing patterns to build machine learning models that accurately forecast global demand for Amazon's tablet devices. By tailoring inventory management closely to consumer preferences, we significantly improved customer satisfaction while optimizing operational efficiency. These forecasts weren't just academic exercises - they directly informed supply chain allocation, not just at Amazon but at retail partners like BestBuy and Target.

Another major initiative involved leading the development of a causal inference model designed to quantify the Potential Sales Lift (PSL) for Selling Partners. This model enabled sellers to measure and optimize the incremental impact of their actions, empowering them with data-driven strategies to enhance sales performance. By doing so, it also improved customer experience through better product discovery and more relevant offerings. The model was validated through A/B testing and drove significant incremental revenue across the Amazon seller ecosystem. Both projects exemplify what I love about data science: blending technical rigor with direct, measurable business value.

Interviewer: You've mentioned trust in modeling being crucial. Can you elaborate on that philosophy?

Hatim: Building a great model is one thing; making people trust it is another. Early in my career, I realized models need to not only perform well but also be explainable, interpretable, scalable, and fair. At Amazon, I led the creation of a guardrail framework that filtered out biased estimates in causal models. That framework improved reliability and built stronger confidence among stakeholders using the data for big decisions. Trust is critical because at the end of the day, decisions - whether it's forecasting millions in inventory or allocating credit - are based on what your model tells people. Without trust, even the best algorithm is useless.

Interviewer: Before Amazon, you held leadership roles at American Express. What lessons did you bring from fintech to big tech?

Hatim: At American Express, I worked on a lot of time-series forecasting for global credit card volumes, especially for stress testing and capital planning. That taught me the importance of robustness under uncertainty. One project that stands out was building a k-Nearest Neighbors (kNN) model to assess financial hardship programs. That model drove significant cost savings and improved customer response strategies - particularly important during the pandemic. What I brought to Amazon was a mentality of business alignment - that your model must serve a well-defined purpose, solve a real need, and be validated end-to-end, including with risk management oversight. That mindset has been critical in scaling projects successfully at Amazon.

Interviewer: You recently co-authored a research paper about customizable credit cards. What's exciting about that?

Hatim: Yes! That project was exciting because it brings together behavioral science, reinforcement learning, and customer-centric design. We proposed a customizable, multi-account credit card framework that lets users tailor their rewards structures, credit terms, and pricing dynamically - guided by reinforcement learning algorithms that continuously learn and adapt from customer interactions to optimize satisfaction, user engagement, and profitability. It's about putting power in the hands of consumers while managing risk intelligently. And in a world increasingly focused on personalization, I believe that's where financial products are heading.

Interviewer: Beyond technical expertise, you're known for your collaborative leadership style. How important is cross-functional collaboration in your work?

Hatim: It's absolutely essential. Building a model is rarely a solo endeavor - you need collaboration across engineering, product, legal, marketing, and business teams. I spend significant time translating technical findings into insights about customer trends and behaviors, closely collaborating with product teams to ensure solutions directly address customer needs and enhance user experiences. Good communication builds alignment, and alignment amplifies impact. I believe that being a technical expert isn't enough anymore. You have to be a bridge between different domains.

Interviewer: What technical skills or approaches have been particularly critical in your success?

Hatim: Technically, deep knowledge of Python, R, SQL, Apache Spark, and cloud platforms like AWS and GCP have been essential. Modeling-wise, skills like causal inference, dimensionality reduction, and ensemble learning methods have been extremely valuable. But equally important is understanding deployment - how to make models scalable, resilient, and monitorable in production environments. In the end, it's not just about building models. It's about building systems that solve real problems and can evolve with time.

Interviewer: How do you see the field of data science evolving in the next few years?

Hatim: Data science is moving from "Can we predict this?" to "How can we influence this responsibly?" Technologies like causal AI, reinforcement learning, and explainable AI will take center stage. At the same time, ethical considerations around bias, privacy, and sustainability will become non-negotiable. I also think there will be a stronger emphasis on low-code/no-code solutions that democratize access to data science tools for non-technical users - empowering businesses to integrate AI even faster.

Interviewer: Last question - what advice would you give to aspiring data scientists who want to make a real-world impact?

Hatim: Two things: First, always understand the problem you're solving. A perfect model that solves the wrong problem is worse than a simple model that solves the right one. Second, communicate your results. If you can't explain your model's impact clearly, it won't matter how good it is. And lastly - stay curious. Technology changes fast, but a curious, problem-solving mindset will always be in demand.

Hatim Kagalwala represents a new generation of data scientists - those who blend technical mastery with business savvy and human-centered values. His career - from financial risk modeling at American Express to groundbreaking machine learning initiatives at Amazon - reflects a commitment to leveraging data science to deeply understand and anticipate customer needs, driving measurable impact through enhanced customer experiences and targeted business strategies.

As AI continues to reshape industries, leaders like Kagalwala are ensuring that innovation remains anchored to purpose: helping businesses grow, empowering users, and building systems that are as trustworthy as they are powerful.

In a world of numbers and noise, Hatim stands out as a voice of clarity, integrity, and vision - and his journey is only just beginning.

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