Data Scientist, Next Gen Recommendation Systems

What you'll do

• Design, build, and evaluate recommendation models for candidate generation, ranking, reranking, and personalization across multiple entity types.
• Develop graph-based models and semantic embeddings, and support batch and low-latency real-time recommendation serving.
• Own end-to-end ML delivery, experimentation, monitoring, and iteration in collaboration with Engineering, Product, MLOps, and business stakeholders.

What they ask for

• 3+ years of experience in data science or applied ML, including shipping production models with measurable impact.
• Strong Python and SQL; experience with large-scale data and distributed compute (Spark/Databricks or equivalent).
• Hands-on experience building recommendation or ranking systems, and experience with embeddings and representation learning.
• ML engineering capability or demonstrated ability to develop it; strong experimentation skills, including designing and analyzing A/B tests.

Nice to have

• Experience with graph-based ML, modern deep learning recommender architectures, or vector search.
• Experience with real-time ML serving, feature stores, contextual bandits, or reinforcement learning.

Pay

$100,000 - $125,000 per year (base salary); 5% variable annual bonus (additional variable compensation)

Tools

Python, SQL, Spark/Databricks, FAISS, ScaNN, Vespa, Milvus, PyTorch/TensorFlow

AI focus

Build graph-based recommendation systems using semantic embeddings, representation learning, graph ML, and retrieval; use AI coding agents routinely to accelerate prototyping, development, and shipping.