We are looking for a hands-on Data Scientist with strong ML and MLOps skills to deliver production-grade analytics solutions in a fast-paced, agile environment.

You will work across the full ML lifecycle from data exploration to model deployment with a focus on supply chain and commercial analytics.

Key Responsibilities :

- Build, deploy, and maintain ML models end-to-end using Azure Databricks and Azure Pipelines

- Write production-quality code in Python/PySpark; contribute actively to team repositories

- Design and scale data pipelines (batch and real-time) using big data technologies

- Collaborate with data and ML engineers on model industrialization and deployment automation

- Translate business problems into modelling solutions and communicate insights to stakeholders

- Research and apply latest ML/AI methodologies; create reusable libraries and documentation

Must-Have Skills :

- Python / PySpark / SQL strong hands-on proficiency

- Supervised & unsupervised ML regression, classification, clustering

- Applied statistics distributions, hypothesis testing, regression

- Git, CI/CD workflows, version control best practices

- Azure cloud Databricks and ADF experience

- MLOps MLflow, Kubeflow or equivalent

Good to Have :

- Time Series / Demand Forecasting

- NLP, Bayesian methods, Causal Inference, Reinforcement Learning

- Docker, Jenkins, Spark/Hive

- Responsible AI, Distributed ML

- Supply chain or retail domain experience

Qualifications :

- B. / B.Tech in Computer Science, Mathematics or related field

- 4+years as a Data Scientist in a production environment

- Experience in supply chain or commercial analytics preferred

- Agile team delivery experience