Senior Manager- Analytics

Way of working: The employee needs to come to office. About Dineout Swiggy Dineout is India’s largest dining-out platform. We serve 100M+ diners across 21,000+ partner restaurants in 34 cities, from QSRs and cafés to casual and fine dining. Diners discover restaurants, book tables, and save up to 40% on their bill by paying through the Swiggy app. Analytics on Dineout is changing. We want questions answered in minutes, root causes found before the weekly review, and analysts spending their time on judgment instead of pulling data. This role leads that shift. What you’ll work on Build the AI analytics layer for Dineout. Set up LLM-powered self-serve so product, category, and revenue teams can ask business questions in plain language and get trustworthy answers on governed data. Own the metric and semantic layer that makes this possible: clean definitions, documented tables, and metric logic that both humans and models can rely on. Automate root-cause analysis. Build agentic workflows that detect movements in GMV, conversion, redemption, and repeat rates, then explain what drove them and flag what needs a human. Forecast and monitor core metrics using ML, with anomaly detection replacing manual dashboard-watching. Design, run, and read experiments. Set the standards for test design, power, and guardrail metrics, and use AI to speed up readouts without cutting corners on rigor. Evaluate what the AI produces. Build eval sets and accuracy checks for generated SQL, summaries, and insights. Know when the model is wrong and make sure stakeholders do too. Turn analysis into decisions. Write clear, opinionated narratives for leadership on diner behaviour, restaurant economics, and offer efficiency. Lead and grow a team of analysts and data scientists, and raise their output by changing how they work, not only what they work on. Shape the roadmap for Dineout analytics with product, engineering, data science, and business leads. Why Dineout Large, messy, real data on how India eats out Direct line from your work to product and business decisions A flat, direct culture where good ideas move fast Merit-based growth and market-leading compensation What you need 7–10 years in analytics or data science, including experience leading a team Strong SQL and Python. You can still go deep on the data yourself. Hands-on experience in the last 1–2 years building with LLMs: APIs, prompting, text-to-SQL, RAG, or agent workflows applied to real analytics problems Solid grounding in statistics and experimentation: significance testing, causal inference basics, common pitfalls in A/B tests Working knowledge of ML for regression, classification, and forecasting Experience with modern data stack tools: dbt or similar semantic/metric layers, a cloud warehouse, and a BI tool A degree in engineering, statistics, economics, mathematics, or a related quantitative field. A master’s is a plus. What we look for You’d rather build a system that answers 100 questions than answer one question 100 times You’re skeptical of AI output and know how to test it You can work without a clear brief and decide what matters You write and speak clearly, and you can disagree with a senior stakeholder using data You’ve driven projects across product, engineering, and business teams and got them shipped You enjoy mentoring and setting a high bar for qualit " We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regards to race, color, religion, sex, disability status, or any other characteristic protected by the law" Visit our tech blogs to learn more about some the challenges we deal with: https://medium.com/swiggy-bytes We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, disability status, or any other characteristic protected by the law.