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 POSO 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: 25 years
- Relevant Experience: Hands-on AI / ML / Data Science implementation
- Education: B.Tech from a Tier 1 or Tier 2 Engineering College
