Ford Business Solutions
Data Scientist
- Compensation
- Not disclosed
- Location
- Chennai
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Data science
- Posted
Skills
Full description
The Supply Chain Analytics division withing Global Data Insights and Analytics (GDIA) 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.
- Develop, validate, and deploy advanced Machine Learning Models Agentic solutions to support supply chain and operations decision-making.
- Apply rigorous model validation techniques, including backtesting, sensitivity analysis, and scenario analysis, to quantify impacts on inventory and cost.
- Write efficient SQL to query, transform, and analyze large and complex datasets.
- 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.
- Education: Bachelor's degree in computer science, Data Science, Statistics, or a related technical field.
- Experience: 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.