Data Scientist

Job Description

We are looking for a highly skilled and motivated Data Scientist with deep experience in building recommendation systems to join our team. This role demands expertise in deep learning, embedding-based retrieval, and the Google Cloud Platform (GCP). You will play a critical role in developing intelligent systems that enhance user experiences through personalized content discovery. Key Responsibilities: Develop, train, and deploy recommendation models using two-tower, multi-tower, and cross-encoder architectures . Generate and utilize text/image embeddings (e.g., CLIP , BERT , Sentence Transformers ) for content-based recommendations. Design semantic similarity search pipelines using vector databases (FAISS, ScaNN, Qdrant, Matching Engine). Create and manage scalable ML pipelines using Vertex AI , Kubeflow Pipelines , and GKE . Handle large-scale data preparation and feature engineering using Dataproc (PySpark) and Dataflow . Implement cold-start strategies leveraging metadata and multimodal embeddings. Work on user modeling , temporal personalization , and re-ranking strategies . Run A/B tests and interpret results to measure real-world impact. Collaborate with cross-functional teams (Engineering, Product, DevOps) for model deployment and monitoring. Must-Have Skills: Strong command of Python and ML libraries: pandas, polars, numpy, scikit-learn, matplotlib, tensorflow, torch, transformers. Deep understanding of modern recommender systems and embedding-based retrieval . Experience with TensorFlow , Keras , or PyTorch for building deep learning models. Hands-on with semantic search , ANN search , and real-time vector matching . Proven experience with Vertex AI , Kubeflow on GKE , and ML pipeline orchestration. Familiarity with vector DBs such as Qdrant , FAISS , ScaNN , or Matching Engine on GCP. Experience in deploying models via Vertex AI Online Prediction , TF Serving , or Cloud Run . Knowledge of feature stores , embedding versioning , and MLOps practices (CI/CD, monitoring). Preferred / Good to Have: Experience with ranking models (e.g., XGBoost , LightGBM , DLRM ) for candidate scoring. Exposure to LLM-powered personalization or hybrid retrieval systems. Familiarity with streaming pipelines using Pub/Sub , Dataflow , Cloud Functions . Hands-on with multi-modal retrieval (text + image + tabular data). Strong grasp of cold-start problem solving , using enriched metadata and embeddings. GCP Stack You’ll Work With: ML & Pipelines: Vertex AI, Vertex Pipelines, Kubeflow on GKE Embedding & Retrieval: Matching Engine, Qdrant, FAISS, ScaNN, Milvus Processing: Dataproc (PySpark), Dataflow Ingestion & Serving: Pub/Sub, Cloud Functions, Cloud Run, TF Serving CI/CD & Automation: GitHub Actions, GitLab CI, Terraform Show more Show less