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Business Intelligence Data Scientist

Stellantis
Posted a month ago, valid for 10 days
Location

Auburn Hills, MI, US

Salary

Competitive

Contract type

Full Time

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Sonic Summary

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  • Stellantis is looking for a Business Intelligence Data Scientist to join their team in Auburn Hills, Michigan, focusing on advanced analytics for business decision-making.
  • The role involves analyzing large datasets to provide insights on warranty, cost, quality performance, and operational effectiveness.
  • Candidates should have a Bachelor’s degree in a quantitative field and relevant internship experience, with a preference for those holding a Master’s degree and possessing experience in predictive analytics.
  • Strong analytical skills, statistical expertise, and the ability to communicate findings effectively to various stakeholders are essential for this position.
  • The salary for this role is competitive, and candidates are expected to have at least some relevant experience, preferably in automotive or manufacturing environments.

Stellantis is seeking a highly skilled Business Intelligence Data Scientist to support advanced analytics within the Business Intelligence and Data Analytics team at the Headquarters & Technology Center in Auburn Hills, Michigan. This role is responsible for applying advanced analytical techniques to large and complex datasets in order to generate actionable insights that inform business decisions related to warranty, cost, quality performance, and operational effectiveness.

The Data Scientist will work with a variety of internal data sources to develop analytical solutions, perform deep exploratory analysis, and support predictive, diagnostic, and prescriptive analytics efforts. The role requires strong analytical thinking, statistical expertise, and the ability to clearly communicate findings to both technical and non‑technical stakeholders.

The successful candidate will collaborate closely with Engineering, Quality, Finance, and IT partners and operate with a high degree of independence in a fast‑paced, data‑driven environment.

 

Key Responsibilities:

  • Analyze large, complex datasets to identify trends, relationships, risks, and opportunities related to warranty and quality performance
  • Develop and apply advanced statistical, analytical, and data science techniques to support business problem‑solving and decision‑making
  • Perform exploratory data analysis and root‑cause investigations to explain performance drivers and anomalies
  • Design, develop, and maintain robust analytical models, metrics, and methodologies
  • Translate complex analytical results into clear insights, recommendations, and visualizations for stakeholders
  • Partner with cross‑functional teams to understand business needs and deliver analytical solutions
  • Ensure analytical outputs are accurate, repeatable, and well‑documented
  • Support continuous improvement of analytics processes, tools, and data usage practices
  • Contribute to the development of standardized reporting, metrics, and best practices across the organization
Qualifications

Basic Qualifications:

  • Bachelor’s degree in a quantitative discipline such as Data Science, Statistics, Computer Science, Applied Mathematics, or a related field
  • Relevant internship experience
  • Demonstrated ability to communicate analytical findings clearly to technical and non‑technical audiences
  • Excellent problem‑solving, organizational, and time‑management skills
  • Ability to work independently with minimal supervision

Preferred Qualifications:

  • Master’s degree in a quantitative discipline
  • Experience applying predictive, diagnostic, or prescriptive analytics techniques in a business environment
  • Familiarity with data visualization and business intelligence tools (e.g., Power BI, Tableau)
  • Experience working in automotive, manufacturing, quality, or operational analytics environments
  • Experience with working across multiple deployment environments including cloud, on-premises and hybrid, using multiple operating systems
  • Experience translating complex data into clear narratives for leadership decision‑making



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