Job description
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
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