Selected product work across ETA prediction, routing, dispatch quality, safety, captain growth, product operations, payments, and AI-assisted product discovery.

Delivery reliability comes from prediction quality, routing accuracy, dispatch timing, restaurant readiness, courier movement, and the promise shown to the customer.

I work on the systems behind delivery reliability: ETA quality, routing accuracy, dispatch behavior, supply-demand balance, operational reviews, and customer-facing recovery moments. The practical question is whether customers can trust what we show them, and whether the marketplace can keep that promise consistently.

Sustained captain supply depends on onboarding quality, earning confidence, engagement loops, loyalty mechanics, and marketplace demand working together.

I worked across the captain lifecycle — acquisition, activation, engagement, retention, and loyalty — with a focus on the quality of supply we could actually rely on during busy periods and high-risk markets.

Mobility safety depends on clear user-facing surfaces, reliable escalation paths, responsible use of mobile signals, and enough transparency for riders, drivers, and trusted contacts to act with confidence.

I led safety product work across rider and driver experiences, including Safety Centre, mobile telematics, driver safety insights, and Share My Ride. A lot of the work was about making safety easier to understand and act on in the moments when riders, drivers, or trusted contacts needed it.

Strong product execution depends on shared health metrics, clear rollout decisions, disciplined experimentation quality, and teams that can move quickly without losing context.

I improved the way product teams operated: marketplace health reviews, rollout discipline, experimentation routines, faster analytics workflows, and clearer cross-functional decision-making. The goal was to help teams make better calls earlier — what to ship, what to test, what to scale, and what to stop.

I use AI to reduce slow handoffs across analysis, synthesis, and documentation: SQL and dashboard exploration, problem-space exploration, qualitative synthesis, clearer PRD drafting, and pressure-testing decisions before they reach engineering or leadership forums. It helps me get to a sharper first version faster, while keeping the actual product judgment with me.

AI speeds up preparation and iteration; I still own the framing, trade-offs, customer understanding, and final judgment.

Local payment methods and payment rails are shaped by scheme rules, settlement models, certification paths, operational constraints, and customer expectations around availability and speed.

I scoped payment method and rail-level integrations across Northern Europe, working through scheme requirements, infrastructure constraints, and rollout dependencies so product and engineering teams had a clearer path to execution.

Swiggy was around two years old when I joined, and I was at the very beginning of my product management journey.

That combination mattered. The company was scaling quickly, the operating model was still being built, and many product decisions had to be made close to the ground: customer expectations, merchant behavior, delivery partner constraints, support patterns, and business priorities all had to be understood together.

That experience shaped much of my early product intuition. It taught me that product decisions are rarely only interface decisions; they are tied to merchant behavior, delivery constraints, support patterns, and business priorities. It also gave me the early high of seeing messy, operational problems turn into products that people actually used.