Role Overview :

We're looking for a hands-on Data Scientist who builds and ships production-grade ML solutions across forecasting, classification, regression, and Natural language usecases. You take ownership end-to-end, ramp up fast on new domains, and measure success by business outcomes, not just model accuracy.

What You'll Do :

- Build and deploy enterprise ML models forecasting, classification, regression, anomaly detection, and recommendations.

- Own end-to-end ML pipelines : data ingestion, feature engineering, training, validation, and production deployment.

- Apply embeddings, vector search, and representation learning for similarity, search, and retrieval-augmented workflows.

- Drive MLOps : CI/CD for models, containerisation, versioning, A/B testing, retraining, and drift monitoring.

- Instrument production models with observability latency, data quality, feature drift, and prediction accuracy tracking.

- Translate business problems into well-scoped ML solutions in close collaboration with engineering and product teams.

What You Bring :

- 5-8 years deploying ML models in enterprise or product settings.

- Strong Python and ML stack (scikit-learn, XGBoost/LightGBM, PyTorch/TensorFlow).

- Deep expertise in supervised/unsupervised learning and time-series forecasting.

- Working knowledge of embeddings, vector databases (FAISS, Pinecone, pgvector), and LLM/RAG pipelines is added benefit.

- Hands-on MLOps : Model serving & ML observability.

- Experience with cloud platforms (AWS/GCP/Azure) is a plus.