Data Scientist

Experience: Minimum of 2.5 years required (Range: 2-4 Years).

Role Overview

This role involves investigating the feasibility of applying scientific principles to business problems within the retail sector. You will develop new algorithms independently to solve well-defined retail challenges and enhance existing systems.

• Collaborate with product teams to implement new modules, maintain production pipelines responsibly, and release updates on time.
• Create Proof-of-Concepts (POCs) for innovative ideas or technologies, driving internal and external adoption of analytics solutions in retail.

Key Responsibilities

You will work closely with Data Engineering, Advisory, and Product teams to identify improvement scopes and drive optimal processes. Your day-to-day includes writing efficient code using PySpark or Python (with at least 2.5 years experience), building modular production-grade systems with OOP frameworks and comprehensive test cases.

Technical Requirements

• Mastery of SQL, relational databases, and handling unstructured data like images and text blobs.
• In-depth machine learning knowledge in at least two areas: Regression (classical or Deep Learning), Classification, Text Mining/NLP, or Recommender Systems. A mathematical foundation is essential; DL experience is a plus.

Performance Optimization

You must demonstrate efficient data processing skills, including vectorization and avoiding row-wise operations, along with rudimentary knowledge of asymptotic algorithm analysis.

Toxicity: Good to Have Skills

• Familiarity with GenAI systems (LLMs, RAG, agentic workflows) and the ability to design beyond basic use cases using situational thinking.
• Experience with cloud technologies for model deployment on Azure, AWS, or GCP.
• Theoretical background in Docker and CI/CD practices.

Bonus Skills

Retail experience is highly valued. Even if it constitutes only 30-50% of your overall tenure, having at least one retail project is a strong asset for this role.