Data Scientist

Role overview

Data Scientist at Ford Motor Private Limited

Responsibilities

• Develop, validate, and deploy advanced Machine Learning Models & Agentic solutions to support supply chain and operations decision-making.
• Write efficient SQL to query, transform, and analyze large and complex datasets.

Required qualifications

• Bachelor’s degree in computer science, Data Science, Statistics, or a related technical field.
• 3+ years of professional experience in AI/ML engineering or Software Engineering with a focus on model deployment.
• GenAI Stack: Hands-on experience with LLMs and orchestration frameworks (e.g., LangChain, Langgraph) GCP Proficiency: Working knowledge of Google Cloud Platform, specifically Vertex AI, BigQuery, and Cloud Run.
• Core ML Knowledge: Strong foundation in machine learning theory and practical experience with libraries such as scikit-learn, XGBoost, or PyTorch/TensorFlow.
• Software Engineering: Proficiency in Python and a solid understanding of software best practices, including API design, Git version control, and unit testing.

Employment details

• Location: India

Additional role details

• The Supply Chain Analytics division withing Global Data Insights and Analytics (GDI&A) department at Ford Motors Company is looking for qualified Data Scientists who can develop scalable solutions to complex real-world problems using Machine Learning, AI and Cloud Solutions.
• Apply rigorous model validation techniques, including backtesting, sensitivity analysis, and scenario analysis, to quantify impacts on inventory and cost.
• Build and deploy containerized services and data pipelines on Google Cloud Platform, leveraging Cloud Run, BigQuery, Dataform, and Cloud Build.
• Operationalize models into production workflows, including APIs, scheduled pipelines, and monitoring/alerting for ongoing production support.
• Communicate complex analytical findings clearly through storytelling, visualizations, and presentations tailored to both technical and non-technical audiences.
• Partner with cross-functional stakeholders across supply chain, operations, and engineering teams to drive adoption of analytics products and influence business decisions.