Data Scientist

Job Description

">Data Scientist 2.5-6 Years Bengaluru data science NLP Role -Data /Applied scientist (Search/ Recommendation) Experience - 2.5 yrs to 6 years Location - Bangalore Strong in Python and experience with Jupyter notebooks , Python packages like polars, pandas, numpy , scikit-learn, matplotlib, etc. Must have: Experience with machine learning lifecycle , including data preparation , training , evaluation , and deployment Must have: Hands-on experience with GCP services for ML & data science Must have: Deep understanding of modern recommendation systems including two-tower , multi-tower , and cross-encoder architectures Must have: Hands-on experience with deep learning for recommender systems using TensorFlow , Keras , or PyTorch Must have: Experience generating and using text and image embeddings (e.g., CLIP , ViT , BERT , Sentence Transformers ) for content-based recommendations Must have: Experience with semantic similarity search and vector retrieval for matching user-item representations Must have: Proficiency in building embedding-based retrieval models , ANN search , and re-ranking strategies Must have: Experience with Vector Search and Hybrid Search techniques Must have: Experience with embeddings generation using models like BERT , Sentence Transformers , or custom models Must have: Experience in embedding indexing and retrieval (e.g., Elastic, FAISS, ScaNN , Annoy ) Must have: Experience with LLMs and use cases like RAG (Retrieval-Augmented Generation) Must have: Understanding of semantic vs lexical search paradigms Must have: Experience with Learning to Rank (LTR) techniques and libraries (e.g., XGBoost , LightGBM with LTR support) Should be proficient in SQL and BigQuery for analytics and feature generation Should have experience with Dataproc clusters for distributed data processing using Apache Spark or PySpark Should have experience deploying models and services using Vertex AI , Cloud Run , or Cloud Functions Should be comfortable working with BM25 ranking (via Elasticsearch or OpenSearch ) and blending with vector-based approaches Good to have: Familiarity with Vertex AI Matching Engine for scalable vector retrieval Good to have: Familiarity with TensorFlow Hub , Hugging Face , or other model repositories Good to have: Experience with prompt engineering , context windowing , and embedding optimization for LLM-based systems Should understand how to build end-to-end ML pipelines for search and ranking applications Must have: Awareness of evaluation metrics for search relevance (e.g., precision@k , recall , nDCG , MRR ) Should have exposure to CI/CD pipelines and model versioning practices GCP Tools Experience: ML & AI : Vertex AI, Vertex AI Matching Engine, AutoML , AI Platform Storage : BigQuery , Cloud Storage, Firestore Ingestion : Pub/Sub, Cloud Functions, Cloud Run Search : Vector Databases (e.g., Matching Engine, Qdrant on GKE), Elasticsearch/OpenSearch Compute : Cloud Run, Cloud Functions, Vertex Pipelines , Cloud Dataproc (Spark/ PySpark ) CI/CD & IaC : GitLab/GitHub Actions