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# R&D Data Scientist | Automotive

- **Company:** Capgemini
- **Location:** Diegem (Machelen), Belgium
- **Experience:** Experienced
- **Contract:** Temporary contract
- **Skills:** Data Analysis, Factor Analysis, Python (Programming Language), Machine Learning, Feature Engineering, Model Validation
- **Published:** August 19, 2026
- **Apply:** https://www.capgemini.com/jobs/539528-en_GB_SAPBTP/x/

## About the Role

* Technical depth: Strong proficiency in Python for analysis, modelling and prototyping, combined with solid foundations in statistics, machine learning and AI - including feature engineering, dimensionality reduction and model validation.
 * Applied research capability: Able to read and critically assess technical material, adapt methods to real-world constraints, and design experiments on complex, ambiguous datasets - going well beyond running standard pipelines.
 * Engineering sensitivity: You understand what your data physically and behaviourally represents, and can judge whether results make sense in context - not just statistically, but meaningfully.
 * Communication and autonomy: You work independently, structure your own analyses, challenge weak or ambiguous results, and clearly communicate assumptions, limitations and conclusions to diverse audiences.
 * Fluent in English
 * Minimum 4 years of experience

## Description

* Analysing vehicle telemetry, driving data, and unstructured data sources (including verbatim text) to build interpretable features and dimensions that define driving patterns and behaviours.
 * Linking real-world telemetry data with survey responses, customer personas, and behavioural indicators to generate a rich, multi-dimensional understanding of driver profiles.
 * Reviewing relevant academic and technical literature, assessing applicability to the project, and rapidly testing approaches through proof-of-concepts on real, messy data.
 * Comparing and validating analytical methods - including clustering, dimensionality reduction, factor analysis, and supervised/unsupervised modelling - with rigorous attention to robustness, reliability, and limitations.
 * Collaborating with engineers to ensure findings are technically grounded, and communicating results clearly with well-reasoned recommendations to both technical and non-technical stakeholders.


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