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Data Scientist

Ford Global911 open roles

Where
Chennai, Tamil Nadu, India
Work mode
On site
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Your applicationOpen nowData ScientistFord Global · Chennai, Tamil Nadu, India

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Ford Global's own form.

The reply lands in your private mailbox

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The clock on this job

Early applications get read.

13.7% of postings close within 7 days. Measured by our own scanner across the market. Ford Global postings stay open a median of 13 days.

Share of postings closed within
  1. 2.3%1 day
  2. 4.8%3 days
  3. 13.7%7 days
  4. 22.1%14 days
  5. 37.6%30 days
  6. 59.1%60 days
This job: posted today

Ford Global median: 13 days open

The posting

Job Description

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

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.

Qualifications

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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