Data Scientist

Role Overview: The Data Scientist will own the end-to-end AI roadmap and execution across business functions. This is a hands-on role focused on building, deploying, and scaling AI-driven solutions that enable automation, accuracy, and smarter decision-making. The role works closely with leadership to translate business priorities into high-impact, production-ready AI systems. Key Responsibilities: AI Strategy & Roadmap Define and execute the company's AI roadmap aligned with business goals. Partner with cross-functional teams to identify automation and intelligence opportunities. Drive adoption of AI for real-time decision-making and operational efficiency. Forecasting & Demand Intelligence DD Forecasting: Build SKU-level and city-level forecasting models integrating client stock and inventory data for production, sales, and offtake planning. D2C Forecasting: Develop self-learning AI models for SKU-level sales forecasting across channels. Demand Sensing: Predict inter-city movement of goods using live and external data sources. Pricing, Liquidation & Revenue Optimization Design dynamic pricing engines using automated scraping across multiple e-commerce platforms, including pincode-level optimization. Build D2C liquidation systems triggered by real-time stock, demand, and pricing signals. Automation & Computer Vision Implement FnV quality check automation using image-based AI for QC pass/fail decisions. Develop auto listing engines to enable D+2 product listings using scraped images, prices, and availability data. Create Auto PO–SO conversion systems to read purchase orders and generate corresponding sales orders. NLP, Analytics & Insights Build customer feedback analytics using NLP across app, Amazon, and Google reviews. Develop finance reconciliation automation to match GRN, PO, SO, POD, and payments and flag discrepancies. Generate NPD insights by mining trends from YouTube, Google, and Amazon data. Design influencer identification models using social and engagement analytics. Advanced Use Cases & Innovation Implement packaging simulation models to analyze global design trends and recommend optimal packaging strategies. Continuously improve model accuracy, scalability, and production readiness. Qualifications and Experience: Total Experience: 2–5 years Relevant Experience: Hands-on AI / ML / Data Science implementation Education: B.Tech from a Tier 1 or Tier 2 Engineering College