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