Data Scientist

Location - Delhi (Hybrid) The Role The Data Scientist will turn messy, multi-party network data into models and insights that improve discovery, fulfilment, trust, and the overall health of the network. Data Scientist will collaborate closely with product, engineering, and policy teams, and your work will directly affect millions of buyers and hundreds of thousands of small sellers across the country. This is a hands-on, individual-contributor position suited to someone with a few years of applied experience who is ready to own problems end-to-end — from framing and data wrangling through modeling, evaluation, and working with engineers to ship.
Key Responsibilities
Build and evaluate models across one or more network problem areas — search relevance and ranking, recommendations, demand forecasting, logistics ETA and matching, fraud and quality, or pricing intelligence.
Work with partial, distributed data. Design analyses and models that are robust to the fragmented visibility inherent in a decentralized network, where no single participant sees the full buyer-to-delivery journey.
Tame real-world data. Normalize heterogeneous seller catalogs, resolve duplicate listings, and handle noisy, multilingual, code-mixed queries and inconsistent Indian addresses.
Apply causal and experimental thinking. Help measure the true effect of product and incentive changes using experimentation and causal-inference methods, accounting for non- stationarity driven by policy.
Translate analysis into decisions. Define metrics, build dashboards, and communicate findings clearly to product, engineering, and leadership stakeholders.
Ship responsibly. Partner with ML and platform engineers to productionize models, and uphold the network's commitments to privacy, fairness, and explainability — especially where models affect small sellers.
Monitor and improve. Track model and network health, investigate failure points in the transaction funnel, and iterate based on observed outcomes.
Required Qualifications
Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Economics, or a related quantitative field.
3–5 years of applied data science experience delivering models or analyses that shipped to production or directly informed decisions.
Worked on projects using various classification techniques like logistics regression, decision trees, gradient boosting, k-means clustering, recommendation models and time series forecasting models.
Preferred Qualifications
Experience in e-commerce, marketplaces, logistics, fintech, or other large-scale consumer platforms.
Exposure to building agentic frameworks is a plus but only if it was a meaningful project that you can explain
Required Skills
Strong programming in Python and fluency with the data/ML stack (e.g., pandas, NumPy, scikit-learn, and at least one deep-learning framework such as PyTorch or TensorFlow).
Solid SQL and comfort working with large datasets and distributed data tools.
Sound grounding in statistics, machine learning fundamentals, and rigorous model evaluation.
Experience with experimentation (A/B testing) and an understanding of when correlation is not causation.
Preferred Skills
Hands-on work in one or more of: search/ranking, recommender systems, time-series forecasting, fraud/anomaly detection, or operations research / optimization.
Familiarity with NLP and entity resolution, particularly for multilingual or noisy text.
Skills: python,pytorch,sql

Work arrangement
No