Data Analyst - Advanced Analytics Finance (m/f/d)

As a Data Analyst in the Advanced Analytics – Finance team, you will help develop forecasting, agentic AI, and analytics solutions that improve Finance operations. You’ll work with Data & AI and Finance colleagues to advance the analytics lifecycle from data understanding to deployment and monitoring. This role offers hands-on data science experience with tangible business impact in a globally distributed environment. You’ll contribute to measurable outcomes while expanding practical skills in a supportive team. This is a chance to shape finance analytics at Adidas through responsible AI and governance-aware solutions.

• Support development, testing and improvement of data science methods for Finance use cases, focusing on forecasting and predictive analytics
• Explore, clean and prepare structured and unstructured data, assess data quality and document assumptions
• Build and evaluate machine learning and statistical models using supervised and unsupervised techniques
• Contribute to time series forecasting, model validation, experiment design and analysis, and interpret model performance
• Translate analytical findings into actionable recommendations and identify new analytics opportunities
• Adhere to reproducible development practices, including version control, code review, testing and documentation
• Support design, prototyping, testing and improvement of agentic AI solutions for Finance use cases
• Evaluate agent outputs for accuracy and usefulness and identify risks such as hallucinations and prompt injection
• Create clear visualizations and concise narratives that explain model results and business implications
• Present analyses to Finance, Data & AI and Tech teams, adapting detail to audience
• Share learnings and reusable code within the data science community
• Collaborate with Finance owners and Tech teams on data access, governance and deployment
• Support adoption of analytics solutions through demonstrations and documentation

• At least 3 years of IT experience; 1 year in a relevant area
• Strong foundation in statistics, data preparation, model evaluation and common ML methods
• Working knowledge of Python or R (Python preferred) and SQL
• Experience with pandas, scikit-learn, statsmodels or similar libraries
• Familiarity with Git and reproducible software development practices
• Foundational understanding of generative AI and LLM concepts (prompting, embeddings, RAG, tool use)
• Hands-on exposure to building or evaluating a GenAI or agentic AI prototype is an advantage
• Awareness of responsible AI topics (hallucination risk, privacy, security, bias, human oversight)
• Familiarity with cloud/big data environments (AWS, Databricks, Spark) is an advantage
• Awareness of MLOps concepts; Docker or DL frameworks (PyTorch/TensorFlow) a plus
• Exposure to optimization, simulation or what-if analysis is beneficial
• Experience with agile ways of working is an advantage
• Excellent English communication; ability to explain analytics to technical and non-technical audiences
• Curious, collaborative, detail-oriented and solution-focused

• Curious and collaborative mindset
• Structured and solution-oriented approach
• Strong communication for cross-functional audiences
• Python or R
• SQL
• pandas, scikit-learn, statsmodels