Stellantis

Data Scientist - Commercial Analytics

Stellantis • $90K — $130K *
Auburn Hills, MI 48326In-Person
Business Services
Less than 5 years of experience
Today
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Job Overview by Ladders

Qualifications

  • Bachelor's degree in a quantitative discipline (Statistics, Economics, Computer Science, etc.)
  • Minimum of 3 years of experience in data science or econometrics
  • Proficient in Python and SQL
  • Experience with big data technologies and cloud platforms (Databricks, Snowflake, Spark)
  • Familiarity with MLOps practices and model deployment
  • Strong knowledge of machine learning algorithms like regression, causal models, and tree-based models
  • Ability to convert complex data into practical business insights

Responsibilities

  • Collaborate with business stakeholders to pinpoint high-impact statistical and machine learning opportunities.
  • Develop thorough methodologies that are robust enough for executive review and strategic guidance.
  • Clearly convey complex results to both technical and non-technical audiences.
  • Work with data engineers to determine necessary data features and ensure proper data utilization.
  • Create and validate predictive models using various techniques including regression and neural networks.
  • Present findings and actionable recommendations through effective visual storytelling.
  • Maintain models in production, ensuring they are scalable and perform well.

Benefits

  • Collaborative work environment that promotes data science best practices
  • Opportunities for professional growth in commercial analytics
  • Access to advanced big data technologies and cloud platforms
  • Engagement with cross-functional teams to influence business outcomes
  • Support for ongoing learning in machine learning and statistical methodologies
Full Job Description
The Commercial Analytics team is looking for a Data Scientist to join our team. Your mission is to build and scale trusted data science products that power commercial performance measurement and growth while promoting data science best practices, actionable recommendations and a high bar for model quality and reliability.

Data scientists work closely with data engineers, analysts, and business teams to design analytics solutions, implement advanced algorithms and evaluate the performance of use cases. Ideal candidates are self-motivated, inquisitive and creative, with a strong desire to solve real-world problems using data.

In this role, you will:
  • Collaborate with business stakeholders to identify high-impact opportunities for statistical and machine learning use cases.
  • Develop defensible, well-documented methodologies that stand up to executive scrutiny and support strategic decision-making.
  • Communicate complex results clearly to both technical and non-technical audiences.
  • Partner with data engineers to define and source relevant data features for modeling as well as drive adoption and a deep understanding of proper data usage.
  • Develop and validate predictive models using techniques such as regression, random forests, gradient boosting, causal modeling and neural networks.
  • Communicate findings and recommendations to non-technical audiences through clear visualizations and storytelling.
  • Contribute to the maintenance of models in production environments, ensuring scalability and performance.
  • Conduct peer code reviews and support best practices in model development and deployment.
  • Collaborate with both external and internal resources to support business requirements and key KPI measurement


Basic Qualifications:
  • Bachelor's degree in a quantitative discipline (e.g., Statistics, Economics, Computer Science or other quantitative field)
  • Minimum of 3 years of experience in data science, econometrics or a related field
  • Proficiency in Python and SQL
  • Hands-on experience with big data and cloud platforms such as Databricks, Snowflake or Spark
  • Exposure to MLOps best practices, including model versioning, monitoring, and deployment pipelines
  • Strong grasp of machine learning algorithms like:
    • Regression (linear, logistic)
    • Causal Inference Models (Difference-in Difference, Regression Discontinuity Design)
    • Tree-based models (Random Forest, XGBoost, LightGBM)
    • Neural networks
    • Clustering and dimensionality reduction (e.g., LDA, PCA, Dynamic Time Warping)
  • Ability to translate complex data into actionable insights for business stakeholders

Preferred Qualifications:
  • Master's degree in a quantitative discipline (e.g., Statistics, Economics, Computer Science or other quantitative field)
  • Automotive experience
  • 2+ years of experience working with commercial data
  • Experience using PySpark for distributed data processing and feature engineering
  • Strong communication and storytelling skills with the ability to influence decision-makers
  • Understanding of CI/CD workflows for automating model testing and deployment
  • Experience working with real-time data pipelines and event-driven architectures
  • Experience with experimental design, and statistical inference
  • Exposure to feature stores and model registries in MLOps environments
  • Experience with Power BI or similar tools for data visualization and dashboarding

About Stellantis

Stellantis is a multinational automotive manufacturer formed in 2021 by the merger of Fiat Chrysler Automobiles and Groupe PSA. The company designs, produces, and sells a wide range of vehicles under various brands, including Alfa Romeo, Chrysler, Citroen, Dodge, DS Automobiles, Fiat, Jeep, Lancia, Maserati, Opel, Peugeot, Ram, and Vauxhall. Stellantis operates in over 130 countries and has 14 brands in its portfolio. The company is committed to sustainable mobility and has set ambitious targets for reducing its carbon footprint and increasing the share of electric vehicles in its sales.
Learn more about Stellantis
Size
400,000 employees
Market Cap
$44.9 billion
Industry
Manufacturing & Automotive
* Ladders Estimates

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