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Verkor is a French industrial company pioneering low-carbon battery manufacturing in France, backed by Renault Group, Plastic Omnium, and Schneider Electric. The company operates the Verkor Innovation Centre (R&D and 4.0 pilot line) in Grenoble and is scaling production at its Gigafactory in Dunkirk to mass-produce low-carbon lithium-ion batteries for European mobility decarbonization.
As a Senior Data Scientist, you will translate business questions into analytical and data science problems, working across production, quality, and digital teams. You will define success metrics aligned with business outcomes, analyze structured and unstructured data to identify patterns and anomalies, and build production-grade automation workflows. Your responsibilities include assessing data quality, completeness, bias, and limitations; identifying additional data sources; performing data transformation for modeling; developing and validating models; and interpreting results in business terms for non-technical stakeholders.
You will document model assumptions, limitations, and risks, collaborate with operators and production/quality technicians to understand objectives and constraints, work with data engineers to operationalize data preparation, and coordinate with data scientists and CAE experts on project roadmap alignment.
Verkor values contribution, transparent communication, team spirit, and commitment to sustainability. The company offers a stimulating multicultural work environment, lunch vouchers, 50% local transportation cost coverage, industry-leading healthcare, and networking opportunities with global experts.
REQUIREMENTS:
- 8–11 years of professional experience
- Master's or PhD in Data Science, Applied Mathematics, or Statistics
- Experience working with large-scale datasets in an industrial environment
- Strong fundamentals in linear algebra, calculus, probability, and statistics
- Solid understanding of distributed systems concepts, data partitioning, and parallel processing
- Hands-on experience with Databricks for data exploration, analysis, feature engineering, and model training
- Strong proficiency in Apache Spark (DataFrames, SQL, MLlib concepts) for large-scale data processing
- Hands-on experience in distributed model training using PyTorch or TensorFlow
- Proficiency with Databricks notebooks (Python/SQL)
- Strong proficiency in pandas and numpy for exploration, feature engineering, and analytical workflows
- Fluency in both English and French (mandatory)
- Knowledge Graph experience is a plus
