Data Scientist
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Key details
- Location
- Chennai, TN, India
- Work style
- Onsite
- Level
- Senior
- Job type
- Full-time
- Experience
- 5–8 yrs
- Function
- Data & ML
- Focus area
- AI / ML
- Posted
- 16h ago
Skills
About the role
About Us
In Global Data Insight & Analytics (GDI&A), we harness the power of data and artificial intelligence to navigate Ford Motor Company through the disruptiveness of the information age. We’re a team of innovators who strive to realize the enterprise’s goals, reveal hidden opportunities, and achieve data superiority.
About You
You are a hands-on Data Scientist with 5+ years of experience applying statistics, machine learning, and analytics to solve business problems. You are skilled at translating ambiguous questions into analytical approaches, exploring complex data, and communicating actionable findings.
You understand data engineering concepts and can work with data pipelines and cloud platforms to prepare reliable datasets and support model deployment. You enjoy collaborating across disciplines and taking ownership from problem definition through measurement of business impact.
What We Offer
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A collaborative and innovative environment focused on using data and AI to solve enterprise challenges.
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Opportunities to work on advanced analytics, machine learning, and modern cloud data platforms.
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A chance to deliver actionable insights and scalable solutions that support Ford Motor Company’s business decisions.
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Professional development, technical upskilling, and opportunities to contribute to analytics and AI best practices.
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A competitive salary and benefits package.
Responsibilities
Partner with business stakeholders to frame problems, define success metrics, and translate business needs into analytical and data science solutions.
Perform exploratory data analysis, statistical analysis, hypothesis testing, and experimentation to identify patterns, drivers, and opportunities.
Design, develop, evaluate, and refine machine learning and statistical models for use cases such as forecasting, classification, segmentation, and anomaly detection.
Apply appropriate validation methods; assess model performance and limitations; and communicate uncertainty, assumptions, and trade-offs.
Prepare, transform, and integrate structured, semi-structured, and unstructured data. Develop reusable features and analytical datasets using Python and SQL.
Collaborate with data engineers to understand and improve data pipelines, data models, quality checks, lineage, and access patterns; contribute to pipeline development where needed.
Partner with ML engineers and engineering teams to operationalize models, automate workflows, and monitor model performance and data drift.
Use cloud data and analytics platforms such as BigQuery and related orchestration or processing tools to access and process data at scale.
Create visualizations, dashboards, and narratives that translate analytical results into actionable recommendations for technical and business audiences.