Chennai, Tamil Nadu
Likely evergreen, first seen by us todayEmployment type in the employer’s posting: Full timeFrom the employer’s posting
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.
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.
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.
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.
Bachelor’s or master’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field.
5+ years of hands-on experience in data science, advanced analytics, applied statistics, or machine learning.
Strong proficiency in Python and SQL for data extraction, preparation, analysis, modeling, and automation.
Solid understanding of statistical inference, hypothesis testing, experimental design, and model validation.
Experience developing and evaluating predictive or statistical models using libraries such as scikit-learn, statsmodels, pandas, NumPy, and SciPy, or equivalent tools.
Experience working with cloud data platforms or enterprise data ecosystems, such as Google Cloud and BigQuery, or equivalent technologies.
Experience deploying or monitoring machine learning models in production and applying MLOps practices.
Experience with data processing or orchestration tools such as Spark, Dataflow, Dataproc, Airflow/Cloud Composer, Dataform, or dbt.
Experience with experimentation, causal inference, optimization, time-series forecasting, or other advanced analytical methods.
Experience with BI or visualization tools such as Looker, Tableau, Power BI, or Python visualization libraries.
Minimum Qualifications - Bachelor’s or master’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field. - 5+ years of hands-on experience in data science, advanced analytics, applied statistics, or machine learning.
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.
In the employer’s words · Read in context
Likely evergreenReposted 3 times since Oct 14, 2026. A role that keeps coming back is often kept open to collect applications rather than to fill one seat.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications. We count a repost when the same title and location comes back as a new listing within 60 days of the last one coming down. Some hiring systems expire listings on a schedule, so repost rates vary by system.
Day 0
Early. Similar jobs usually come down between day 7 and day 57.
At Ford Motor Private, jobs usually come down by about day 2.
From 10,780 similar postings that came down, and 36 of Ford Motor Private’s. Coming down is not always a hire.
2 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- 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.
- 5+ years of hands-on experience in data science, advanced analytics, applied statistics, or machine learning. - Strong proficiency in Python and SQL for data extraction, preparation, analysis, modeling, and automation. - Solid understanding of statistical inference, hypothesis testing, experimental design, and model validation.
- Exposure to generative AI, natural language processing, or unstructured data use cases. - Experience with BI or visualization tools such as Looker, Tableau, Power BI, or Python visualization libraries. - Understanding of cloud performance, scalability, and cost considerations for analytical workloads.
- 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.
- Working knowledge of data engineering concepts, including ETL/ELT, data pipelines, data modeling, data quality, and orchestration. - Experience working with cloud data platforms or enterprise data ecosystems, such as Google Cloud and BigQuery, or equivalent technologies. - Experience communicating insights using visualizations, dashboards, or presentations for business and technical stakeholders.
- Experience deploying or monitoring machine learning models in production and applying MLOps practices. - Experience with data processing or orchestration tools such as Spark, Dataflow, Dataproc, Airflow/Cloud Composer, Dataform, or dbt. - Experience with experimentation, causal inference, optimization, time-series forecasting, or other advanced analytical methods.
- Solid understanding of statistical inference, hypothesis testing, experimental design, and model validation. - Experience developing and evaluating predictive or statistical models using libraries such as scikit-learn, statsmodels, pandas, NumPy, and SciPy, or equivalent tools. - Working knowledge of data engineering concepts, including ETL/ELT, data pipelines, data modeling, data quality, and orchestration.
Complete your application on www.careers.ford.com. The employer’s form will show what is required.
Already applied? Track this application
Yes. We’ve followed 7 other Data Scientist jobs there since June 2026. All 7 are still up.
Large employers often post one title for several teams, so these can be separate openings.
New Data Scientist postings at the employers we follow are down 5% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
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
A collaborative and innovative environment focused on using data and AI to solve enterprise challenges.
Opportunities to work on advanced analytics, machine learning, and modern cloud data platforms.
A chance to deliver actionable insights and scalable solutions that support Ford Motor Company’s business decisions.
Professional development, technical upskilling, and opportunities to contribute to analytics and AI best practices.
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.
Apply reproducible development and MLOps practices, including version control, testing, documentation, and CI/CD, in collaboration with engineering teams.
Follow data governance, privacy, security, and responsible AI practices throughout the analytics and model lifecycle.
Explore and evaluate emerging analytical, machine learning, and generative AI techniques where they address business needs.
Minimum Qualifications
Bachelor’s or master’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field.
5+ years of hands-on experience in data science, advanced analytics, applied statistics, or machine learning.
Strong proficiency in Python and SQL for data extraction, preparation, analysis, modeling, and automation.
Solid understanding of statistical inference, hypothesis testing, experimental design, and model validation.
Experience developing and evaluating predictive or statistical models using libraries such as scikit-learn, statsmodels, pandas, NumPy, and SciPy, or equivalent tools.
Working knowledge of data engineering concepts, including ETL/ELT, data pipelines, data modeling, data quality, and orchestration.
Experience working with cloud data platforms or enterprise data ecosystems, such as Google Cloud and BigQuery, or equivalent technologies.
Experience communicating insights using visualizations, dashboards, or presentations for business and technical stakeholders.
Familiarity with Git, code reviews, documentation, testing, and agile delivery practices.
Strong problem-solving, collaboration, and communication skills.
Preferred Qualifications
Experience deploying or monitoring machine learning models in production and applying MLOps practices.
Experience with data processing or orchestration tools such as Spark, Dataflow, Dataproc, Airflow/Cloud Composer, Dataform, or dbt.
Experience with experimentation, causal inference, optimization, time-series forecasting, or other advanced analytical methods.
Exposure to generative AI, natural language processing, or unstructured data use cases.
Experience with BI or visualization tools such as Looker, Tableau, Power BI, or Python visualization libraries.
Understanding of cloud performance, scalability, and cost considerations for analytical workloads.
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Chennai, Tamil Nadu
Working pattern and location restrictions need checking in the full posting.
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