Data Scientist

About the Role Join AB Volvo’s prognostics team as a Data Scientist to develop predictive, health-based and uptime-related analytics for heavy-duty vehicles. You will own end-to-end models from exploration to production, contributing to both component health monitoring and new uptime services. This role blends data science with domain engineering to deliver real production impact.

What You'll Do

• Build and deliver predictive and prognostic models for component and system-level health monitoring, including failure prediction, wear estimation, and anomaly detection.
• Develop models using vehicle telematics, sensor data, maintenance records, and usage patterns; implement vehicle-level health indicators (degradation scoring, Remaining Useful Life) and alerting.
• Translate model outputs into prescriptive maintenance recommendations and uptime-related services, including fleet-level insights.
• Ensure robust, interpretable, production-ready code with rigorous testing; contribute to the Analytics Pipeline and collaborate with prognostics engineers to define requirements.
• Perform early feasibility assessments to scope analytically possible solutions before full development; hand off to IT and industrialization teams.
• Promote best practices in experimentation, validation, governance; document assumptions, methods, limitations, results, and share knowledge.

What We're Looking For

• Master's degree or higher in Computer Science, Statistics, Mathematics, Engineering, or equivalent.
• Several years of experience in data science in industrial applications.
• Background in mechanical or electrical engineering fundamentals.
• Solid foundations in ML and statistical modelling: regression, classification, time-series, anomaly detection.
• Strong software discipline: Python, SQL, Git; clean and documented code; familiarity with pandas, scikit-learn.
• Experience with big data and cloud environments (Databricks, Spark, Azure).
• Fluent English; interest in how analytics creates value in service contexts such as uptime, maintenance contracts, or fleet operations.

Nice to Have

• Knowledge of OBD/CAN data, ECU logs, or telematics data architectures.
• Experience with survival models, deep learning for time-series (LSTM, Transformers), or unsupervised methods.
• Familiarity with MLOps practices: model versioning, CI/CD, monitoring.
• Curiosity about uptime and aftermarket service business models, and how analytics can enable such offerings.