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This role is the foundation of Accelleron’s new Advanced Analytics AI Center of Excellence. This role requires coding experience and handling of structured and unstructured data sets. This role transforms business challenges into data-driven solutions: identifying the right data, building and validating models, translating results into insights and measurable business value.
The Advanced Analytics AI CoE builds process-agnostic capability and acts as the expert partner to the process domain teams and business units that own the solutions. Alongside hands-on delivery, the role helps the AI citizen program across the company to work safely and effectively with AI. These roles will help to go up to the next AI data maturity level.
Your Responsibilities:
AI and machine-learning use case delivery
- Support and deliver data driven solutions: from problem framing and feasibility assessment to model development, validation, deployment, and post-go-live monitoring.
- Create actionable insights using data science & AI methods: statistical analysis, machine learning, computer vision, Large Language Models applied to business problems.
- Solution Hand over to sustainable operation: documentation, retraining and monitoring approach, and clear ownership together with the process domain and application teams.
Business partnership & value translation
- Work with business units to understand their needs: engage with Divisions & Functions, clarify the actual process to be improved, and challenge when required.
- Translate analytical results into business value and actions: explain findings in business language, quantify the expected benefits, and define what should change as a result.
- Advise business users end to end: from data inputs, feature selection, modelling approaches, evaluation of results, and practical project implementation.
- Contribute to use case qualification: assess data availability and quality, effort, risk and expected return, and support the demand intake and prioritization process. It should follow the regulations in place ( EU AI Act, GDPR) and internal processes ( Security).
Data sourcing & platform collaboration
- Identify and source relevant data: locate the right internal and external data, assess its quality, ownership and suitability, and prepare it for analytical use.
- Work through the Enterprise Data Layer and Data Product Catalogue: consume governed data products when they exist, feed gaps back to the Enterprise Data Architect and data-product owners rather than building one-off extracts (Create once, deploy multiple times).
- Identify and source relevant data: locate the right internal and external data, assess its quality, ownership and suitability, and prepare it for analytical use.
- Work through the Enterprise Data Layer and Data Product Catalogue: consume governed data products when they exist, feed gaps back to the Enterprise Data Architect and data-product owners rather than building one-off extracts (Create once, deploy multiple times).
Tooling, engineering & standards
- Integrate tools such as Python and R into the analytics landscape incl. notebooks, libraries, and their use within Microsoft Fabric and the enterprise data platform.
- Apply sound data engineering practices: version control, code review, environment management, testing, and repeatable pipelines for data preparation and model training.
- Help define the CoE’s methods and standards: reference approaches, templates, model documentation, evaluation criteria, and the path from prototype to production.
- Evaluate new methods and tools: assess their fit for Accelleron and make pragmatic recommendations on what to adopt.
Enablement of citizen data scientists & knowledge sharing
- Enable AI citizen: provide guardrails, templates, training and coaching so business users can apply advanced analytics safely and effectively.
- Advise the citizen community on method choice: when self-service analytics is appropriate, when a CoE-delivered solution is the better answer, and where the risks lie.
- Share best practices and collaborate across Digital and IS teams MS Copilot Studio CoE, process engineers, application owners, and the Digital AI team, to reuse assets rather than duplicate capability.
- Contribute to communities of practice to raise AI & data literacy across the organization.
How success is measured:
- Data Science & AI use cases delivered into production, with demonstrable business value.
- Models that are documented, monitored, and maintainable beyond their original author.
- Analytical solutions built on governed data products rather than bespoke one-off.
- A growing, capable and well-governed AI citizen j4idj8 j4it1044j8 j4iy26j8
