Data Scientist

Responsibilities

• Research, prototype, and implement recommendation models: two-tower, multi-tower, cross-encoder architectures
• Utilize text/image embeddings (CLIP, ViT, BERT) for content-based retrieval and matching
• Conduct semantic similarity analysis and deploy vector-based retrieval systems (FAISS, Qdrant, ScaNN)
• Perform large-scale data prep and feature engineering with Spark/PySpark and Dataproc
• Build ML pipelines using Vertex AI, Kubeflow, and orchestration on GKE
• Evaluate models using recommender metrics (nDCG, Recall@K, HitRate, MAP) and offline frameworks
• Drive model performance through A/B testing and real-time serving via Cloud Run or Vertex AI
• Address cold-start challenges with metadata and multi-modal input
• Collaborate with engineering for CI/CD, monitoring, and embedding lifecycle management
• Stay current with trends in LLM-powered ranking, hybrid retrieval, and personalization

Required Skills

• Python proficiency with pandas, polars, numpy, scikit-learn, TensorFlow, PyTorch, transformers
• Hands-on experience with deep learning frameworks for recommender systems
• Solid grounding in embedding retrieval strategies and approximate nearest neighbor search
• GCP-native workflows: Vertex AI, Dataproc, Dataflow, Pub/Sub, Cloud Functions, Cloud Run
• Strong foundation in semantic search, user modeling, and personalization techniques
• Familiarity with MLOps best practicesCI/CD, infrastructure automation, monitoring
• Experience deploying models in production using containerized environments and Kubernetes

Nice to Have

• Ranking models knowledge: DLRM, XGBoost, LightGBM
• Multi-modal retrieval experience (text + image + tabular features)
• Exposure to LLM-powered personalization or hybrid recommendation systems
• Understanding of real-time model updates and streaming ingestion