Data Scientist

We are looking for two hands-on Senior Data Scientists to design, build, and productionize an advanced recommendation engine on Azure Databricks. The role requires strong expertise in deep learning, recommender systems, embeddings, and production-grade ML.
Responsibilities:
Design and build recommendation models for retention, cross-sell, next-best-action, and quantity prediction.
Develop advanced deep learning recommenders using neural collaborative filtering, two-tower architectures, deep embeddings, sequence-based models, and transformer-based approaches.
Build customer, product, and interaction embeddings for personalised recommendations and ranking.
Develop scalable feature engineering, training, evaluation, and inference pipelines on Azure Databricks.
Design robust evaluation approaches including temporal validation, ranking metrics, explainability, and A/B testing.
Productionize models with MLflow, automated retraining, model monitoring, drift detection, and CI/CD.
Independently own the data science lifecycle from problem formulation through production deployment and continuous improvement.
Requirements:
5+ years of hands-on experience in Data Science and Machine Learning, including production-grade ML solutions.
Advanced degree such as M. Tech, MS, MSc, or PhD in Computer Science, Computing, Mathematics, Statistics, or a related quantitative discipline.
Academic background from a Tier 1 or Tier 2 engineering, science, or technology institution.
Strong expertise in deep learning and modern recommender-system architectures.
Strong hands-on experience with Python, PySpark, SQL, PyTorch or TensorFlow, Azure Databricks, and MLflow.
Strong understanding of embeddings, ranking, personalisation, model evaluation, explainability, MLOps, and production deployment.
Ability to independently architect and build complex ML solutions and work effectively with engineering and business teams.
Good to Have:
Hands-on experience with Generative AI, LLMs, RAG, and embedding-based retrieval.
Exposure to Agentic AI, including multi-agent systems, tool-using agents, and agentic workflows.
Experience integrating GenAI or Agentic AI capabilities with traditional ML and recommendation systems.

Experience
5-9 yrs