Data Scientist - Recommendation Systems

Job Title Data Scientist – Recommendation Systems

Location Bangalore

Experience 3–8 years (flexible based on depth in ML systems)

Job Description

We are looking for a Data Scientist (Recommendations) to design, build, and scale personalized recommendation systems that power discovery, ranking, and user engagement across our products.

Key Responsibilities

Recommendation & ML Design and develop recommendation systems including:

• Collaborative Filtering (user-item, item-item) Content-based and hybrid recommenders
• Ranking and re-ranking models Embedding-based retrieval (ANN, vector search)
• Train, evaluate, and iterate on models using offline metrics (NDCG, MAP, Recall@K) and online A/B experiments Production ML & Systems Optimize inference for scale (caching, batching, approximate nearest neighbors)
• Build real-time and batch recommendation pipelines
• Monitor model performance, data drift, and system health

Data & Experimentation

• Work with large-scale datasets (clicks, impressions, transactions)
• Define success metrics for recommendations (CTR, CVR, retention)

Collaboration

• Work closely with product, data, and backend teams to translate business problems into ML solutions
• Contribute to ML best practices, documentation, and system design

Required Skills

Core ML

• Strong understanding of: Recommendation algorithms Ranking and learning-to-rank
• Embeddings and similarity search
• Experience with Python and ML libraries (PyTorch / TensorFlow / Scikit-learn)
• Data & Systems Strong SQL skills; experience with large datasets
• Familiarity with vector databases / ANN libraries (FAISS, ScaNN, Elasticsearch/OpenSearch KNN, Milvus)

Good to Have

• Experience with: Search or feed ranking systems
• Real-time recommendations
• Knowledge of: MLOps tools (MLflow, Airflow)
• Experience in e-commerce, ads, content platforms or marketplaces

What You'll Work On

• Personalized home feeds and search ranking "People also viewed" recommendations
• Cold-start and long-tail problems
• Large-scale experimentation and model optimization

Nice Behavioral Traits

• Strong problem-solving and system-thinking mindset
• Ability to balance model quality vs production constraints