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