Data Science Support

A typical day might include:

• Identifying, collecting and understanding data sets (structured, semi-structured, unstructured) from R&D, PMO and engineering sources;
• Assessing data quality, clean, transform and prepare for AI analysis or consumption;
• Ensuring document transformation logic, data sources and feature engineering processes;
• Assuring Exploratory Data Analysis (EDA) and statistical analyses to identify trends, correlations and anomalies;
• Developing Machine Learning models (classification, regression, clustering, forecasting) and evaluate their performance;
• Developing prompt engineering and optimization for LLMs, Retrieval-Augmented Generation (RAG) based solutions, document analysis, automatic summarization, information extraction;
• Developing AI agent workflows (if necessary), with reasoning steps, human validation and integration with external tools;
• Creating interactive dashboards in Looker/Looker Studio for KPI monitoring and decision support;
• Generating technical and business reports with clear insights and actionable recommendations;
• Working closely with Product Owners, IS/IT and engineering teams to align solutions to business needs;
• Ensuring compliance with GDPR, security rules and IATF standards;
• Assuring fully document methods, assumptions, limitations and operating instructions for delivered solutions.

Requirements:

• Bachelor’s Degree in Data Science, Statistics, Computer Science, Engineering or related fields;
• Minimum 3 years in Data Science/AI solution development, preferably in the automotive sector or similar industries;
• Required knowledge:
• Python (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch),
• SQL/Big Query for data manipulation at scale,
• Machine Learning (supervised/unsupervised learning, model evaluation),
• Generative AI: Prompt engineering, LLMs, RAG;
• Visualization: Looker/Looker Studio, Tableau or Power BI;
• Google Cloud Platform (AI/ML tools, Vertex AI, Gemini)
• Desired knowledge:
• Agentic AI (workflow orchestration, tool usage);
• PLM (Product Lifecycle Management) and R&D processes;
• Agile/Scrum and iterative development methodologies;
• English: Advanced (written and spoken) – primary working language.

Soft Skills

• Analytical thinking and ability to translate business needs into technical solutions;
• Clear communication of technical results to non-technical stakeholders;
• Teamwork and collaboration with cross-functional departments;
• Attention to detail and respect for confidentiality and security.