Data Scientist Machine Learning & Applied Analytics.

Role & responsibilities

• Translate product and customer problems into well-defined analytics and ML use cases.
• Perform data exploration, feature engineering, statistical analysis and experimentation.
• Develop, train, validate and optimize machine learning and predictive models.
• Define appropriate model performance metrics, benchmarks and acceptance criteria.
• Convert prototypes and experiments into scalable, production-ready capabilities.
• Work with Product, Architecture and Engineering teams to integrate models and analytics into SaaS products and platforms.

Model Evaluation & Productization

• Evaluate models and analytical solutions developed by internal teams, technology partners and specialist solution providers.
• Assess model assumptions, data requirements, accuracy, robustness, explainability and limitations.
• Benchmark alternative models/approaches and provide evidence-based recommendations.
• Establish practices for model versioning, validation, monitoring, drift detection and continuous improvement.
• Ensure that externally sourced models can be effectively integrated, governed and evolved within the product.

Preferred candidate profile

• 5-8 years of relevant experience in Data Science, Machine Learning, Applied Analytics or related roles.
• Experience building ML/analytics capabilities that have been deployed into production products or platforms.
• Experience working with automotive, mobility, connected vehicle, battery/EV, IoT or industrial datasets is desirable but not mandatory.
• Experience collaborating with technology partners, research organizations or academic institutions is an advantage.
• Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering or a related quantitative discipline preferred.