Senior Data Scientist-Quick Commerce

About The Role

Responsibilities

• Leverage SQL and Python (Pandas, Spark) to transform large-scale data into operational insights.
• Conduct exploratory data analysis (EDA), build statistical models, and generate actionable insights to influence strategic decisions.
• Design and implement end-to-end data and ML solutions; build and optimize models for demand forecasting and operational efficiency in quick commerce environments.
• Collaborate with stakeholders to understand business requirements and translate them into analytical solutions.
• Work cross-functionally with Product, Operations, and Engineering teams to design and test data-driven solutions that enhance speed, reliability, and customer satisfaction.
• Leverage agentic AI to build intelligent, autonomous decision-making systems for operational and customer experience improvements.
• Innovate, develop, and implement workflow automation using LLM-based automation to streamline data pipelines and decision processes.
• Partner with data engineering teams to design scalable data models and robust data pipelines that ensure high-quality, real-time data availability.
  

Minimum Qualifications

• Bachelor's degree in Computer Science, Statistics, Mathematics, or a related field.
• 4+ years experience with pandas, Spark, or similar technologies.
• Expertise in advanced SQL and spreadsheets.
• Hands-on experience building and evaluating ML models with strong theoretical foundations in ML algorithms (e.g., regression, classification, feature selection).
• Working knowledge of statistical analysis and machine learning.
• Experience with dashboards, data visualizations, or big-data analytics products.
• Excellent problem-solving and analytical skills, with a passion for optimizing algorithms and models for high performance.
• Strong communication and collaboration skills, with the ability to work effectively within a diverse, cross-functional team.
  

Preferred Qualifications

• Experience designing models for high-frequency, short-horizon demand forecasting in dynamic environments (quick commerce, food delivery, or e-grocery).
• Strong background in supply chain analytics, including inventory optimization, demand forecasting, and logistics planning.
• Hands-on experience in inventory optimization, replenishment planning, or automated PO/TO systems.