Data Scientist II

About The Role

What will you get to do here?

• Design, develop, and deploy scalable optimization algorithms for supply chain, assortment planning etc.
• Translate business problems into mathematical formulations using tools like Linear Programming, Integer Programming, and Heuristics.
• Build and improve predictive models using ML techniques (regression, classification, time-series forecasting).
• Collaborate closely with Product, Engineering, and Operations teams to take models from prototype to production.
• Analyze model performance and continuously iterate to improve accuracy and efficiency.
• Drive measurable impact on key business metrics such as availability, wastage, revenue per order, cost per order etcWork on high-impact, real-world problems at massive scale.
• Be a part of a fast-growing vertical within Swiggy with opportunities to innovate and experiment.
• Collaborate with a world-class team of engineers, product managers, and data scientists.
• Competitive compensation and a culture that promotes learning, ownership, and growth.
  

What qualities are we looking for?

• 2–5 years of experience in Data Science or Applied Research roles.
• Strong foundation in Operations Research and Mathematical Optimization (e.g., LP, MILP, ILP).
• Solid hands-on experience with Python, SQL, and at least one optimization solver (e.g., Gurobi, OR-Tools, CPLEX).
• Good understanding of Machine Learning concepts and frameworks (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
• Experience in solving large-scale real-world problems using a combination of ML and OR techniques.
• Strong problem-solving skills and ability to work in a fast-paced, collaborative environment.
• Excellent communication and stakeholder management skills.
• Experience in supply chain, logistics, food delivery, or q-commerce/e-commerce domains.
• Prior experience in building real-time or near real-time decision systems.
• Experience working with large datasets and distributed systems (e.g., Spark).
  

Visit our tech blogs to learn more about some the challenges we deal with: