Achieving machine learning at scale requires more than just accuracy — it demands real-world impact. Few professionals understand that better than Hatim Kagalwala, an Applied Scientist at Amazon whose work has shaped strategic decisions across both e-commerce and financial services.
At Amazon, Hatim has developed metrics like Potential Sales Lift and Traffic Lift, which together have resulted in significant, material impact in incremental revenue for third-party sellers. He also worked on building the Downstream Impact metric, now widely used by multiple teams to assess the long-term effects of seller programs. Earlier in his career, as the first data scientist at Credibility Capital, Hatim built the company’s initial credit risk model for small business loans. His work helped the company attract new capital by demonstrating a data-driven approach to risk management.
What sets Hatim apart is not only his technical expertise, but also his ability to build solutions that serve both scientific and business goals. Whether he’s designing credit models for underbanked markets or applying causal inference techniques to seller growth, he works across disciplines to deliver tools that are practical, explainable, and built to scale. In this interview, he talks about balancing complexity with usability, the future of GenAI in applied settings, and what defines an effective data scientist today.
Can you tell us about your role at Amazon and the key projects you’ve been involved in? What does it personally mean to you to work on products that impact millions of sellers?
I work as an Applied Scientist at Amazon, where I focus on developing machine learning and AI solutions that operate at the global scale. Since joining Amazon four years ago, my work has spanned several domains. I began with demand forecasting for Amazon devices, where I developed models that optimized inventory planning and reduced waste across global markets. I then moved into credit risk modeling for emerging markets, where I built systems that expanded responsible access to credit for customers with limited or no traditional credit histories.
In my current role, I use causal inference techniques to help millions of third-party sellers understand the drivers of their growth and make informed business decisions. For example, one of the key outcomes of this work has been the development of metrics like Potential Sales Lift and Downstream Impact, which have been adopted across Amazon and delivered significant, measurable impact in incremental revenue for third-party sellers.
What motivates me is the opportunity to deliver tangible impact at scale. Whether it’s improving forecasting accuracy, helping individuals gain access to credit, or empowering sellers with actionable insights, these solutions directly support individuals and businesses around the world. Knowing that my research and innovations benefit millions at this scale inspires me to keep advancing AI to solve complex and meaningful challenges.
Can you tell us more about the metrics you worked on that help sellers grow their businesses? How do these metrics, such as Potential Sales Lift and potential traffic lift, help sellers make decisions and improve their outcomes?
The Potential Sales Lift and Potential Traffic Lift metrics estimate the incremental sales or traffic a seller might gain from specific actions — like running promotions, enhancing product content, or adopting targeted advertising strategies.
By quantifying the expected impact of different choices, these tools help sellers prioritize how to invest their time and resources. For example, they can determine whether improving product detail pages or increasing advertising spend will drive greater returns. At Amazon, these models inform evaluation of seller-facing programs and help teams prioritize high-impact initiatives.
I must say that building them required close collaboration across science, product, and engineering teams to ensure the solutions were not only accurate but also operationally scalable across regions and product lines. But more than anything, what I find most rewarding after all this work is seeing how these models help entrepreneurs and businesses compete and grow sustainably, often transforming how they scale operations and reach new customers. Contributing to tools that are so broadly adopted and high-impact is a key motivation for my work in advancing applied machine learning.
How challenging was it to implement these innovations within a company as large and complex as Amazon? And how did they influence decisions about future investments in seller programs?
It was definitely a significant challenge to implement these innovations at Amazon, given the vast scale and complexity of the organization. The models needed to work seamlessly across multiple business units, process massive volumes of real-time data, and deliver insights that could be trusted by a diverse range of stakeholders—from product teams to senior business leaders. Ensuring that the models were both accurate and scalable required not only technical rigor but also extensive collaboration with cross-functional teams in engineering, product, and science across geographies.
Despite these complexities, the successful deployment of these models demonstrated the value of evidence-based decision-making at scale. The metrics they power—like Potential Sales Lift and Potential Traffic Lift—became integral to inform program evaluation and prioritization for third-party sellers. By providing reliable estimates of incremental outcomes, these tools helped guide decisions on which seller programs to expand, optimize, or further invest in, ensuring that resources were directed toward initiatives with the highest measurable impact.
For me, overcoming these challenges underscored the importance of building solutions that are not just technically sound but also operationally impactful. Contributing to tools that shape strategic decisions at scale reinforced my commitment to advancing applied machine learning in ways that drive meaningful business and societal outcomes.
I understand that before joining Amazon, you worked at Credibility Capital, where you developed the company’s first credit risk model and a real-time loan portfolio monitoring system—solutions that were seen as industry-transforming. How did these innovations change the company’s approach to risk management and investor engagement?
Credibility Capital is a company that specializes in business loans from $10,000 to $350,000. That first credit risk model that I developed enabled systematic evaluation of borrower risk by integrating both traditional credit bureau data and alternative data sources. By replacing largely manual underwriting with a data-driven approach, we improved the speed and accuracy of credit decisions while ensuring responsible lending practices.
In addition to that, I also built a real-time loan portfolio monitoring system that offered continuous visibility into portfolio performance and identified emerging risks before they could escalate. These innovations supported the management of a loan portfolio exceeding $100 million, enabling proactive risk mitigation and more informed strategic planning.
Together, these solutions transformed risk management by embedding analytics throughout the lending lifecycle—from origination to portfolio oversight. They also strengthened investor confidence by providing transparent, data-driven insights into performance, which helped the company secure additional institutional funding and scale its operations more effectively.
For me, these projects were defining because they demonstrated how advanced analytics can mitigate risk, build investor trust, and lay the foundation for sustainable growth.
You combine deep technical expertise in machine learning with business intuition and strategic influence. How do you strike the right balance between model complexity and practical applicability?
It starts with understanding the problem to solve and the decisions the model needs to support. While I enjoy exploring state-of-the-art machine learning approaches, I focus first on making sure the model delivers actionable insights that are both trusted and usable by stakeholders. This means carefully weighing the benefits of added complexity—like marginal gains in accuracy—against factors such as interpretability, computational cost, and deployment speed.
For example, when working on credit risk modeling at Credibility Capital, I considered advanced ensemble methods but ultimately selected a model that balanced strong predictive power with explainability, so business leaders and regulators could clearly understand and trust the results. Similarly, with causal inference models for sellers, I designed solutions that scale efficiently and produce results that non-technical stakeholders can easily interpret and act on.
In practice, this balance comes from close collaboration with business and product teams, understanding their decision-making needs, and translating complex techniques into tools that drive measurable impact. My goal is always to ensure that the models I build not only perform well technically but also enable confident, data-driven decision-making.
What new technologies or approaches in machine learning and data analysis do you see as most promising for the future of e-commerce and financial services? What’s likely to be most exciting in the near future?
One of the most transformative emerging developments is the application of generative AI in both e-commerce and financial services. While traditional machine learning has been highly effective at prediction and optimization, generative models enable entirely new capabilities—such as creating personalized marketing content at scale, dynamically optimizing product listings, and accurately simulating customer behavior to improve model training where real data is limited.
In e-commerce, generative AI can deliver hyper-personalized shopping experiences by tailoring recommendations, product descriptions, and visual content to individual preferences in real time. For financial services, these models can enhance fraud detection by simulating rare but high-risk events, accelerate credit underwriting by enriching sparse data profiles, and power conversational agents that improve customer engagement while reducing operational costs.
Beyond generative AI, I also see significant potential in combining causal inference with reinforcement learning to support the long-term optimization of decisions—particularly in areas like customer acquisition, retention, and risk management. This combination allows companies not only to understand why outcomes occur, but also to act on that knowledge to maximize lifetime value and minimize risk over time.
What excites me most is how these technologies will evolve from experimental tools into scalable, production-ready systems. The next few years will likely be defined by advancements that make generative AI safer, more interpretable, and more seamlessly integrated into existing workflows—accelerating adoption across myriad industries.
Finally, how do you see the role of a data scientist today? What skill or quality do you believe truly sets apart a strong professional in your field—technical depth, the ability to “speak business,” or something else?
The role of a data scientist today extends well beyond writing code or building models. A strong data scientist must integrate technical depth with the ability to translate insights into business impact. The most successful professionals in this field are not only skilled in machine learning, statistics, and data engineering — they also understand how their work fits into the broader business context. They align solutions with strategic goals and communicate complex findings in a way that drives decisions.
While technical expertise is essential, what truly sets a strong data scientist apart is the ability to bridge disciplines — linking advanced analytics to business goals, product priorities, and stakeholder needs. This combination allows data scientists to drive initiatives that are not only scientifically sound but also operationally relevant and impactful.
Equally important is adaptability. With fields like generative AI, causal inference, and large-scale optimization evolving rapidly, the best professionals continually adapt and apply new approaches while maintaining a focus on the responsible and ethical use of data.
I also believe in following curiosity and solving real-world business problems—not just staying on-trend. Early in my career, I explored different areas until I found my niche at the intersection of machine learning and business impact. Working across disciplines and aligning technical work with strategic needs has helped me identify where I can drive the most value.
In my view, it is this blend of technical mastery, strategic thinking, and adaptability that defines the most impactful data scientists today.
