- Posted 17 hours ago
- Be among the first 10 applicants
Job Description
As a Data Scientist at Reliance Retail, you will work on high-impact, applied data science problems across online and offline commerce across a broad and evolving set of problem spaces. These are organized into the following thematic clusters (you may specialize in one or rotate across multiple):
1. Customer, Growth & Commerce Intelligence
- Search, ranking, recommendations, and personalization
- Customer profiling, segmentation, LTV, churn, and loyalty
- Acquisition, engagement, retention, and lifecycle optimization
- Marketing effectiveness, attribution, uplift, and experimentation
2. Monetization, Pricing & Promotions
- Dynamic pricing and markdown optimization
- Promotion effectiveness and offer personalization
- Ads relevance, bidding intelligence, and measurement
- Margin optimization and revenue intelligence
3. Supply Chain, Planning & Availability
- Demand planning and forecasting at scale
- Inventory, replenishment, and allocation optimization
- Network planning, S&OP, and fulfillment intelligence
- Availability, selection depth, and service-level optimization
4. Store, Operations & Physical Retail Intelligence
- Store efficiency, labor optimization, and compliance
- Planogram intelligence and assortment effectiveness
- Computer vision, video, and sensor-based intelligence
- Shrinkage, fraud, and loss prevention
5. Platform, Automation & Next-Gen AI
- Intelligent decision engines embedded into workflows
- Agentic systems for automated planning, execution, and monitoring
- Causal, simulation, and digital twin–based modeling
- Reusable ML platforms, feature stores, and inference systems
More Info
Key Skills
Reusable ML platforms
Inventory replenishment
Agentic systems
Demand planning and forecasting
Customer profiling segmentation
Dynamic pricing
LTV churn and loyalty
Promotion effectiveness
Marketing effectiveness attribution
Causal simulation
Ads relevance bidding
Intelligent decision engines
Acquisition engagement retention
Search ranking recommendations




