Vacant job

Who are we?

Volvo Cars is a company on a mission; to bring traditional car manufacturing into a connected, sustainable and smart future. Since 1927, we have been a brand known for our commitment to safety, creating innovative cars that make life less complicated for our consumers. In 2010, we decided to transform our business, resulting in a totally new generation of cars and technologies, as well as steady growth and record sales. Today, we’re expanding our global footprint in Europe, China and the US, and we’re on the lookout for new talent. We are constantly pushing our own skills and abilities to drive change in the automobile industry like never before. We are looking for innovative, committed people to join us in this endeavour and create safe, sustainable and connected cars. We believe in the power of people and will challenge and support you to reach your full potential. Join us and be part of Volvo Cars’ journey into the future.

Let's introduce ourselves

This role will join Commercial & Marketing Data, a team at the center of how Volvo Cars turns data into commercial impact across the full customer journey, from first interest to lasting loyalty. We are responsible for advancing data maturity, insights, and data science across the commercial landscape by building reusable, governed, and high-quality data products that make decision-making and product development more scalable, reliable, and effective.

What you'll do

As a Data Scientist in our Commercial Analytics & Data Science team, you will apply data science and machine learning to create decision-grade insights and predictive signals that improve how we understand and serve customers across key journeys. Your responsibilities will include:

• Build, validate, and maintain predictive models that generate decision-grade signals across commercial customer journeys (e.g., churn and propensity), and adapt the modeling focus as priorities evolve (e.g., acquisition, conversion, retention, CRM/campaign optimization, channel optimization).
• Translate business questions into measurable targets, features, and modeling approaches (classification, survival/time-to-event, uplift/causal approaches when applicable).
• Engineer and curate features from enterprise customer and commercial datasets (e.g., identity resolution, event histories, ownership/service signals, digital behavior, CRM/campaign touchpoints, channel interactions), in collaboration with data engineering and domain teams.
• Partner with stakeholders (product, marketing, CRM, sales, customer care, analytics) to turn model outputs into decision-grade signals (scores, segments, triggers) that can be used in reporting, experimentation, and activation.
• Productionize models with clear documentation, reproducibility, and governance—ensuring model assumptions and limitations are understood.
• Contribute to the long-term customer analytics / commercial modeling roadmap: what we model, why, and how we standardize the approach across use cases — with Customer 360 as a key near-term initiative.

What you'll bring

We believe you’re a curious, pragmatic, and collaborative data scientist who can connect modeling work to real business decisions.

Required / strongly preferred:

• Strong experience in applied machine learning for customer analytics (churn, propensity, segmentation, scoring).
• Solid statistical foundations (experimental thinking, bias/variance, calibration, confidence/uncertainty, causal pitfalls).
• Proficiency in Python for data science (pandas, scikit-learn and/or similar) and SQL for analytics.
• Experience working with large-scale data in a modern data platform (e.g., Snowflake / cloud data warehouse environments).
• Ability to communicate clearly with both technical and non-technical stakeholders; comfortable explaining model performance, tradeoffs, and limitations.

Nice to have:

• Experience with time-to-event / survival analysis, uplift modeling, or causal inference methods.
• Experience deploying models into production (batch or near-real-time scoring), plus monitoring and alerting.
• Experience with feature stores, ML pipelines, or MLOps practices.
• Familiarity with Customer 360 / identity resolution / customer master data concepts.

Ways of working

• You will work in a cross-functional environment with data engineers, analytics engineers, product partners, and domain experts.
• You will be based in Stockholm and collaborate closely with stakeholders across Volvo Cars.