Data Science
Actively Reviewing the ApplicationsReliance Retail
India, Maharashtra, Navi Mumbai
Full-Time
On-site
Posted 14 hours ago
•
Apply by June 5, 2026
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)
- 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
- Monetization, Pricing & Promotions
- Dynamic pricing and markdown optimization
- Promotion effectiveness and offer personalization
- Ads relevance, bidding intelligence, and measurement
- Margin optimization and revenue intelligence
- 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
- 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
- Platform, Automation & Next-Gen AI
- Intelligent decision engines embedded into workflows
- Agentic systems for automated planning, execution, and monitoring
- Causal, simulation, and digital twinbased modeling
- Reusable ML platforms, feature stores, and inference systems
Required Skills
Forecasting
Automation
Compliance
Simulation
Monitoring
Computer Vision
Loss Prevention
Data Science
Supply chain
Pricing
Demand planning
Profiling
Segmentation
Modeling
Attribution
Embedded
Retail intelligence
Marketing effectiveness
Shrinkage
Network planning
Physical
Measurement
Revenue Intelligence
Labor
Demand
Experimentation
Churn
Inference
Monetization
Sensor
Fraud
Customer Profiling
Data scientist
Margin Optimization
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