Stellantis

Data Scientist

Stellantis • $110K — $130K *
Auburn Hills, MI 48326In-Person
Information Technology
5 - 7 years of experience
Today
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Job Overview by Ladders

Qualifications

  • Bachelor's degree in a quantitative field such as Operations Research or Data Science.
  • Minimum of 5 years experience in fields like operations research or data science.
  • Proficient in Python and SQL programming languages.
  • Hands-on experience with big data and cloud platforms like Databricks or Snowflake.
  • Familiarity with MLOps best practices including model monitoring and deployment.
  • Strong understanding of various mathematical modeling techniques.

Responsibilities

  • Collaborate with business stakeholders to identify impactful statistical and machine learning opportunities.
  • Develop well-documented methodologies for strategic decision-making.
  • Communicate complex results to technical and non-technical audiences.
  • Partner with data engineers to define relevant data features for modeling.
  • Develop and validate models using advanced techniques such as regression and neural networks.
  • Contribute to model maintenance in production environments for performance enhancement.
  • Conduct peer code reviews to uphold best practices in model development.

Benefits

  • Collaborative work environment with cross-functional teams.
  • Opportunities to contribute to high-impact commercial analytics projects.
  • Possibility of engaging with both internal and external business resources.
  • Access to advanced tools and technologies like big data and cloud platforms.
Full Job Description
Job Overview:

The Commercial Analytics group is looking for a Data Scientist to join our team. Your mission is to build and scale trusted data science products that power commercial recommendations while promoting data science best practices, actionable outputs 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 models using techniques such as regression, optimization (linear programming, dynamic programming, etc.), gradient boosting, dimensionality reduction 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., Operations Research, Applied Mathematics, Optimization, Data Science or other quantitative field)
  • Automotive experience
  • Minimum of 5 years of experience in operations research, systems engineering, data science, 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 mathematical concepts like:
    • Regression (linear, logistic)
    • Linear & Non-Linear Programming
    • Network Flow Models
    • Dynamic Programming
    • Simulation
    • Stochastic Optimization
    • Tree-based models (Random Forest, XGBoost, LightGBM)
    • Neural networks
    • Clustering and dimensionality reduction (e.g., LDA, PCA, Dynamic Time Warping)
  • Experience communicating optimization tradeoff and recommendations to executive stakeholders

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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