Data Scientist
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Key details
- Location
- Chennai, TN, India
- Work style
- Onsite
- Level
- Mid level
- Job type
- Full-time
- Experience
- 4–7 yrs
- Function
- Data & ML
- Focus area
- AI / ML
- Posted
- 2d ago
Skills
bigqueryci/cddata miningdata sciencegcpgenerative ailookermachine learningmlopspower bipythontableau
About the role
As a Data Scientist in Ford’s Global Data Insights and Analytics (GDI&A) team, you’ll use machine learning to solve business and engineering problems across areas such as design cost, connected vehicles, manufacturing, and product quality. You’ll work with large-scale data, develop and evaluate models, and apply Generative AI where it adds value. Using GCP and working closely with cross-functional teams, you’ll turn your findings into insights and solutions that can be put to use.
- Build data-driven models to understand the characteristics of engineering systems
- Apply machine learning, data mining and text mining techniques to create scalable solutions for business problems
- Train, tune, validate, and monitor predictive models
- Analyze and extract relevant information from large amounts of historical business data especially related to quality, product development, and connected vehicles, both in structured and unstructured formats
- Establish scalable, efficient, automated processes for large scale data analyses
- Package and present the findings and communicate with large cross-functional teams
- Bachelor’s or advanced degree in Data Science, Computer Science, Statistics, Applied Mathematics, Engineering, or a related field.
- 4+ years of hands-on data science experience, with strong Python and machine learning skills.
- Demonstrated experience developing Generative AI applications, not solely using AI tools as an end user.
- Hands-on experience with GCP, particularly BigQuery and Vertex AI.
- Experience building dashboards using tools such as Looker, Power BI, Tableau, or similar.
- Strong problem-solving, communication, and stakeholder-collaboration skills.
- Preferred, not required: experience deploying models or applications through APIs, and familiarity with MLOps practices such as versioning, CI/CD, monitoring, and automated deployment.
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