The Data Scientist role focuses on partnering with business stakeholders to identify opportunities where machine learning and analytics can support data-driven decision-making across a variety of customer-focused use cases. This role is central to data exploration, insight generation, agile model development, and translating results into clear, actionable recommendations for non-technical audiences.

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.

As a Data Scientist, You Will:

Collaborate with business stakeholders to identify high-impact opportunities for analytics and machine learning use cases
Translate business questions into analytical problems and develop models to support personalization, journey orchestration, loyalty, customer lifetime value, and customer segmentation
Design and implement test-and-learn frameworks to measure the impact of data-driven initiatives
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, 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

Requirements:

Basic Qualifications:

Bachelor's degree in a quantitative field (e.g., Data Science, Computer Science, Statistics)
3+ years of experience in data science, analytics, or a related field, with a Bachelors
Proficiency in Python and SQL
Experience using PySpark for distributed data processing and feature engineering
Familiarity with Customer Data Platforms (CDPs) and integrating customer data across systems
Exposure to MLOps best practices, including model versioning, monitoring, and deployment pipelines
Strong grasp of machine learning algorithms including (2-3 at least):
- Regression (linear, logistic)
- Tree-based models (Random Forest, XGBoost, LightGBM)
- Neural networks
- Clustering and dimensionality reduction (e.g., LDA, PCA)
- Recommender systems (e.g., collaborative filtering)
Experience with A/B testing, experimental design, and statistical inference.
Ability to translate complex data into actionable insights for business stakeholders

Preferred Qualifications:

Master's degree in a quantitative discipline (e.g., Data Science, Computer Science, Statistics or Engineering)
Automotive experience
2+ years of experience working with large-scale customer datasets
Experience with customer journey analytics, lifetime value modeling, or propensity scoring
Familiarity with CRM systems and marketing analytics tools / data
Strong communication and storytelling skills with the ability to influence decision-makers
Experience working with real-time data pipelines and event-driven architectures
Exposure to feature stores and model registries in MLOps environments
Experience with Industrial Engineering and Supply Chain modeling / analytics
Hands-on experience with big data and cloud platforms such as Databricks, Snowflake, Hadoop or Spark
Understanding of CI/CD workflows for automating model testing and deployment
Experience with Power BI or similar tools for data visualization and dashboarding

Stellantis

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Data Scientist