In today’s data-driven world, few professionals embody the intersection of analytics, innovation, and business impact as effectively as Hatim Kagalwala. With over six years of experience in the fields of machine learning, statistical modeling, and applied data science, Kagalwala has made substantial contributions to some of the most recognized global enterprises, including Amazon and American Express. From a mechanical engineering graduate in Mumbai to an applied scientist at Amazon’s Seattle headquarters, his path attests to a career driven by academic rigor, strategic thinking, and a love for solving practical issues.
A Career Built on Innovation and Insight
Kagalwala’s career path is defined by a constant emphasis on leveraging data to propel corporate value. Currently serving as an Applied Scientist II at Amazon, his contributions have significantly influenced financial product innovation, digital retail, and global supply chain strategies. Notably, he led the development of machine learning models to forecast worldwide demand for Amazon’s tablet devices—enabling smarter supply chain allocation across Amazon-owned and third-party retail channels such as BestBuy and Target. This work resulted in substantial annual cost savings and improved operational efficiency at scale.
But Kagalwala’s impact extends far beyond cost efficiency. One of his most influential projects involved the development of a Causal Inference model using Double/De-biased Machine Learning to surface Potential Sales Lift (PSL) for Amazon’s Selling Partners. This innovative model empowered partners to identify high-impact opportunities while enabling internal teams to quantify downstream business impact at scale. The introduction of PSL was validated through rigorous A/B testing, which revealed significant gains in incremental revenue, driving measurable business value across Amazon’s seller ecosystem.
Advancing Data Science at Scale
Kagalwala’s approach is not just about building models—it’s about building trust in those models. He led the development of a scalable guardrail framework that filtered out biased estimates, significantly enhancing the reliability of Amazon’s internal metrics and strengthening stakeholder confidence. His credit modeling work—particularly the use of advanced algorithms like CatBoost and innovative proxy signals to power new credit offerings in Amazon’s emerging marketplaces—further showcases his ability to combine statistical rigor with real-world impact. The resulting model demonstrated strong predictive performance, supporting more inclusive and data-driven lending decisions.
Moreover, Kagalwala has played an important role in thought leadership within Amazon. His methodologies and models have been featured in several internal and global conferences, reinforcing his standing as both a practitioner and a scholar. Most recently, he co-authored a research paper introducing a novel framework for customizable, multi-account credit cards—an idea that merges behavioral personalization with reinforcement learning–based pricing to reshape the future of credit products. The paper breaks new ground by blending user-driven design, dynamic rewards structures, and optimization techniques to align cardholder satisfaction with issuer profitability—addressing both complexity in financial management and sustainability concerns in a single solution.
Leadership at American Express
Before joining Amazon, Kagalwala held a leadership position at American Express as Manager of Data Science in the Credit and Fraud Risk Finance division. There, he led the development of time-series forecasting models for credit card volumes across global portfolios, enabling the company to make data-informed investment and capital adequacy decisions. These forecasts were particularly critical during periods of macroeconomic uncertainty and played a key role in financial stress testing.
He also engineered a k-Nearest Neighbors (kNN) model to evaluate internal financial hardship programs, delivering substantial annual cost savings and improving the company’s ability to respond to changing customer credit behavior—especially in the wake of the pandemic. His models were not only technically robust but also aligned with business needs, receiving validation from oversight bodies such as the Model Risk Management Group (MRMG) and the Model Review Committee (MRC), and were actively used by senior leadership through executive dashboards he built in Tableau and PowerBI.
A Foundation in Finance and Engineering
Kagalwala’s transition into data science was shaped by a robust academic foundation. After earning his Bachelor of Engineering in Mechanical Engineering from the University of Mumbai with a GPA of 3.7, he pursued a Master’s in Financial Engineering with a data science focus at New York University’s Tandon School of Engineering. This education equipped him with the statistical acumen and financial modeling capabilities required to excel in both fintech and big tech environments.
His early career at Credibility Capital, a fintech lending platform, offered a preview of his potential. There, he built a LASSO-regularized logistic regression model that significantly improved default prediction accuracy and boosted loan applicant volumes through innovative web scraping tools and lead generation pipelines. Even at this formative stage, Kagalwala showed a knack for blending data science with business development.
Technical Fluency and Cross-Functional Collaboration
Kagalwala’s technical expertise spans a wide range of languages and frameworks, including Python, R, Scala, and SQL, with deep proficiency in tools such as Apache Spark, Scikit-learn, Tableau, Django, and cloud platforms like AWS and GCP. His experience extends across the full machine learning lifecycle—from scalable data processing and model development to deployment and monitoring in distributed cloud environments.
On the modeling front, his toolkit includes advanced statistical methods such as dimensionality reduction, causal inference, and neural networks, as well as specialized financial techniques like Monte Carlo simulations, derivatives pricing, and term structure modeling.
What truly sets Kagalwala apart is his ability to translate complex analytical insights into actionable strategies. He consistently bridges the gap between data science and business, enabling collaboration across engineering, product, and executive teams. His work is not just technically rigorous—it’s operationally embedded, strategically aligned, and built to deliver impact at scale.
You can explore his technical projects and ongoing work through his LinkedIn profile, which highlights his professional journey and ongoing contributions to the data science community.
Beyond the Numbers: A Passion for People
Though technically gifted, Kagalwala is more than just a data scientist—he is a collaborator and a communicator who believes data science should serve human-centered goals. Whether enhancing the seller experience on Amazon’s Seller Central platform or building credit models to support economic inclusion in underbanked regions, his work consistently reflects a commitment to using data for meaningful impact.
His leadership style emphasizes mentorship, open collaboration, and a culture of shared innovation. By fostering environments where ideas flow freely and people feel empowered, Kagalwala has become a trusted and influential contributor in every organization he’s joined.
A Lasting Impact
Hatim Kagalwala’s career provides a model for how technological brilliance, commercial alignment, and human-centric ideals can merge to produce observable, scalable influence as the domains of data science and artificial intelligence change. Kagalwala has always transformed difficult data into actionable insights driving development and innovation, from financial services to e-commerce, from demand forecasting to democratizing access to credit.
With a career still unfolding, the future looks promising for Kagalwala. But one thing is already clear: his influence is not only embedded in the systems and models he builds, but also in the value and opportunity they unlock for businesses and people around the world.
