Senior Data Scientist

Accountabilities

• Immerse yourself in automotive industry data to uncover patterns, trends, and insights that can improve predictive analytics and support new data products.


• Design, develop, evaluate, optimize, and deploy advanced AI/ML and classical regression models for production use.


• Lead complex end-to-end data science projects, taking ownership from problem definition and feature engineering through modeling, validation, deployment, and ongoing improvement.


• Develop innovative machine learning processes and document methodologies, assumptions, workflows, and results to ensure that analytical outputs remain accurate, transparent, and reproducible.


• Collaborate closely with technology and engineering teams to improve existing processes, develop new capabilities, and support reliable production deployment.


• Partner with product teams to create analytics and predictive capabilities that power new products and address important challenges within the automotive market.


• Use advanced feature engineering and statistical techniques to improve model performance and uncover meaningful relationships within large datasets.


• Leverage Python, R, SQL, Snowflake, and relevant machine learning frameworks for advanced data manipulation, modeling, and analysis.


• Develop visual reports and dashboards in Tableau to communicate findings and analytical outcomes to internal and external audiences.


• Contribute ideas, technical approaches, and modeling strategies within the data science team while continuously exploring ways to improve predictive algorithms.


• Conduct ad-hoc data analysis, reporting, and investigations to answer emerging business and product questions.

Requirements

• Bring at least 8 years of overall professional experience with a Bachelor’s degree, or at least 6 years of experience with a graduate degree, including substantial hands-on data science experience.


• Have at least 4 years of experience working with large datasets and applying statistical or analytical methods to complex data problems.


• Have 4+ years of experience with statistical programming and data science tools, with strong Python or Spark experience preferred and R or SAS experience considered a plus.


• Have at least 4 years of experience working with database technologies such as MS SQL, Snowflake, or MySQL.


• Demonstrate strong knowledge of machine learning, statistical modeling, advanced feature engineering, model evaluation, and predictive analytics.


• Have practical experience taking machine learning models into production, ideally including cloud-based MLOps environments and API-based model deployment.


• Be comfortable working with cloud infrastructure and modern machine learning frameworks, with the ability to build scalable and maintainable analytical solutions.


• Be able to communicate technical concepts clearly to non-technical audiences and translate complex analytical findings into understandable business insights.


• Bring strong presentation skills and the ability to share analytical results effectively with colleagues, stakeholders, and clients.


• Demonstrate curiosity, creativity, and a genuine passion for solving challenging problems through data.


• Work collaboratively across teams, actively contribute ideas, and build strong working relationships with technical and business stakeholders.


• Experience with automotive market data, large-scale predictive analytics, or related industry datasets is a strong advantage.


• Experience using AI assistants such as Claude within machine learning or data science projects is a plus.


• Shareable examples of data visualizations or analytical work are valued.


• A PhD in Statistics, Data Science, Economics, or a related field is an additional advantage.


• Candidates must be legally authorized to work in Spain, as employment sponsorship is not provided for this position.

• Opportunity to work on data products with direct impact on automotive industry decisions and market analytics.


• Significant ownership over advanced machine learning projects, from initial concept through production deployment.


• Access to modern technologies and the freedom to explore innovative approaches to data science and machine learning.


• Collaborative environment with close interaction across data science, engineering, technology, and product teams.


• Virtual-first working environment with flexible work arrangements.


• Opportunities for continuous learning, professional development, and growth as both a technical expert and collaborative professional.


• Culture centered on innovation, collaboration, data-driven decision-making, execution, trust, and accountability.


• Opportunity to contribute ideas and shape predictive algorithms and analytical products used by internal and external stakeholders.


• Work within an experienced and established data science team where knowledge sharing and technical excellence are encouraged.

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