Data Scientist

Reliance Retail; Bengaluru Urban; On-site; INR 900,000 - 1,200,000; Full time

Job summary

A leading retail company in India seeks a Data Scientist to tackle high-impact data science issues spanning online and offline commerce. The role involves engaging with diverse thematic areas such as customer profiling, dynamic pricing, and inventory optimization. Ideal candidates should possess a strong background in data analysis, machine learning, and statistical modeling. This is an excellent opportunity to contribute to innovative projects in a dynamic environment.

Qualifications

• Proficiency in data analysis and statistical modeling.
• Experience with machine learning algorithms and frameworks.
• Ability to visualize and communicate data insights effectively.

Responsibilities

• Solve high-impact data science problems across various clusters.
• Specialize or rotate across thematic areas like customer profiling and dynamic pricing.
• Optimize inventory and demand planning through advanced modeling.

Skills

Data analysis

Machine learning

Statistical modeling

Data visualization

Education

Master's degree in Data Science or related field

Tools

Python

R

SQL

TensorFlow

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):

• 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


• 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