Data Scientist

Location: Bengaluru (Hybrid)

Role Summary

We’re seeking a skilled Data Scientist with deep expertise in recommender systems to design and deploy scalable personalization solutions. This role blends research, experimentation, and production-level implementation, with a focus on content-based and multi-modal recommendations using deep learning and cloud-native tools.

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 practices—CI/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