Data Scientist

Key Responsibilities:

• Design, build, and maintain scalable, reliable data pipelines to support AI/ML models and analytics in global supply chain and planning operations.

• Collaborate with data scientists and AI engineers to prepare and transform structured and unstructured data for modeling and simulation.

• Develop and optimize ETL/ELT processes using tools like Databricks, Spark.

• Ensure data quality, versioning, and lineage across the ML lifecycle.

• Support the deployment and monitoring of AI models in production environments.

• Translate business requirements into technical solutions, working closely with cross-functional teams.

• Contribute to the development of data services and APIs that power intelligent decision-making tools.

Functional Knowledge

• Masters Degree in Data Science, Computing, AI/ Analytics

• 7-10 years of expertise in SQL, Python, and distributed data processing (e.g., Spark, PySpark).

• Experience with cloud-based data platforms (Azure).

• Experience with LLM and Agentic AI

• Familiarity with MLOps tools and practices (e.g., MLflow) is a strong advantage.

• Experience working with AI/ML frameworks (e.g., TensorFlow, PyTorch) or supporting model training and deployment is a strong advantage.

• Understands the role of data in driving supply chain and planning decisions.

• Able to align data engineering solutions with business KPIs and operational goals.

• May lead technical workstreams or mentor other engineers.

• Takes ownership of data infrastructure components and contributes to architectural planning.

• Solves complex data challenges with creativity and precision.

• Proactively identifies and resolves data quality, performance, and scalability issues.

• Enables the success of AI/ML initiatives by ensuring robust, scalable, and high-quality data infrastructure.

• Supports real-time and predictive decision-making across global operations

• Communicates effectively with data scientists, AI engineers, and business stakeholders.

• Comfortable working in a fast-paced, collaborative environment with evolving priorities.