Data Analyst - Advanced Analytics Finance

Purpose & Overall Relevance for the Organization:

The Data & AI function includes an Advanced Analytics – Finance team that works closely with the Global Finance organization on strategically relevant use cases with the potential to deliver substantial value and improve the way Finance operates. Examples include a financial forecasting engine designed to make forecasting faster, more efficient and more responsive, as well as agentic AI products that help Finance users access trusted information, analyze data, automate multi-step workflows, and support decision-making within clearly defined governance and human-oversight boundaries.

As a Data Analyst in the Advanced Analytics – Finance team, you will support the development and continuous improvement of forecasting, agentic AI and other analytics solutions. Working with more experienced data scientists, Finance colleagues and Tech partners, you will contribute across the analytics lifecycle, from business and data understanding through data preparation, modelling and evaluation to deployment and monitoring. The role provides an opportunity to build and develop strong practical data science skills while contributing to measurable business outcomes.

Key Responsibilities:

Data science and analytics

o    Support the development, testing and improvement of data science methods for Finance use cases, with a particular focus on forecasting and predictive analytics.
o    Explore, clean and prepare structured and unstructured data, assess data quality and document assumptions and limitations.
o    Build and evaluate machine learning and statistical models using appropriate supervised and unsupervised techniques.
o    Contribute to time series forecasting, model validation, experiment design and analysis, and the interpretation of model performance.
o    Translate analytical findings into clear, measurable recommendations and support the identification and initial assessment of new analytics opportunities.
o    Follow reproducible development practices, including version control, code review, testing and documentation.

Agentic AI and intelligent Finance products

o    Support the design, prototyping, testing and continuous improvement of agentic AI solutions for Finance use cases, including AI agents that retrieve information, analyze data, generate summaries and coordinate defined workflow steps.
o    Contribute to solutions that combine large language models with governed enterprise data, APIs, analytical models, retrieval-augmented generation and workflow orchestration.
o    Evaluate agent outputs for factual accuracy, relevance, consistency and business usefulness, and help identify risks such as hallucinations, inappropriate tool use, prompt injection and unintended disclosure of information.

Data visualization and storytelling

o    Create clear visualizations and concise narratives that explain model results, key drivers, uncertainty and business implications.
o    Present analyses and insights to Finance, Data & AI and Tech colleagues, adapting the level of detail to the audience.
o    Share learnings, reusable code and good practices within the data science community and actively learn from internal and external networks.

Stakeholder collaboration

o    Work with Finance business owners and project teams to understand requirements, clarify analytical questions and incorporate feedback.
o    Collaborate with Tech and other Data & AI teams on data access, platform integration, governance, deployment and operational support.
o    Support adoption of analytics solutions through demonstrations, user documentation and responsive follow-up.

Key Relationships:

o    Finance project teams and business owners
o    Global and market Tech teams
o    Other Data & AI teams

Education and Experience / Minimum Qualifications:

o    University degree in Data Science, Machine Learning, Statistics, Econometrics, Computer Science, Mathematics, Engineering or a related quantitative discipline. 
o    Practical exposure to data science through internships, working-student roles, university projects, a thesis, open-source contributions or comparable hands-on experience.
o    Initial experience with time series forecasting or predictive modelling is an advantage.
o    Basic understanding of Finance concepts such as Profit & Loss, Free Cash Flow or Balance Sheet is an advantage, but not required.
o    At least 3 years of experience in IT 
o    1 year of experience in relevant area

Technical skills

o    Good foundation in statistics, data preparation, model evaluation and common machine learning methods.
o    Working knowledge of Python or R. Python is preferred; willingness to learn additional tools and languages is expected.
o    Experience with SQL and common data science libraries such as pandas, scikit-learn, statsmodels or comparable tools.
o    Exposure to Git and reproducible software development practices.
o    Foundational understanding of generative AI and large language model concepts, such as prompting, embeddings, retrieval-augmented generation, tool use or agent workflows.
o    Initial hands-on exposure to building or evaluating a generative AI or agentic AI prototype through university work, an internship, a personal project or comparable practical experience is an advantage.
o    Awareness of responsible AI topics such as hallucination risk, privacy, security, bias, human oversight and the need to evaluate AI-generated outputs.
o    Familiarity with cloud or big data environments such as AWS, Databricks or Spark is an advantage.
o    Awareness of MLOps concepts, model deployment, monitoring, Docker or deep learning frameworks such as PyTorch or TensorFlow is beneficial, but not required.
o    Exposure to optimization, simulation or what-if scenario analysis is beneficial.
o    Familiarity with agile ways of working is an advantage.

Soft skills

o    Proficient spoken and written command of English.
o    Ability to explain analytical work in a structured and understandable way to both technical and non-technical colleagues.
o    Curious, collaborative and willing to learn, with a structured and solution-oriented approach.
o    Attention to detail and willingness to challenge assumptions, model outputs and AI-generated responses using data, evidence and structured evaluation.
o    Comfortable working in cross-functional and international teams and asking for guidance when needed.