AI Data Scientist

As an AI Data Scientist, you will assess, shape, and support high-value AI opportunities across the business, with a strong focus on Generative AI use cases in enterprise environments. You will work closely with business stakeholders, Business Analysts, and AI engineering teams to evaluate feasibility, refine solution direction, and ensure AI initiatives are practical, valuable, and ready for delivery. You will also contribute to machine learning and advanced analytics use cases by designing models, documenting logic, and supporting transparent, governed deployment.

What You'll Do and How You'll Succeed

Generative AI Use Case Assessment & Shaping

• Collaborate with business stakeholders and Business Analysts to identify, define, and prioritise Generative AI opportunities.
• Conduct front-end feasibility and scope assessments by evaluating use case suitability, data readiness, risk considerations, and value potential.
• Filter out mis-scoped or infeasible requirements before AI engineering engagement to ensure clean and actionable handovers.

Generative AI Solution Research & Design

• Partner with Generative AI engineers to research, design, and refine AI solutions that address business needs.
• Research the latest Generative AI trends, emerging technologies, and market best practices.
• Contribute to enhancements of enterprise-level AI frameworks, playbooks, and standards.
• Apply industry best practices to model design, evaluation, and iterative solution optimisation.
• Analyse model and solution behaviours to identify limitations such as misinterpretations, hallucinations, edge-case failures, or inconsistent responses, and work with engineers to improve them.
• Contribute insights that support continuous enhancement of AI solutions through informed, iterative development.

Business Validation & Collaboration

• Support Business Analysts and users during UAT for Generative AI solutions.
• Advise business users on Generative AI solution behaviour and likely root causes of observed behaviour.
• Work closely with Technology and AI Engineering teams to clarify issues, validate fixes, and ensure solutions meet business intent and evaluation criteria.

Machine Learning & Advanced Analytics

• Design and develop analytical and machine learning models for automation, insights, and predictive use cases.
• Use enterprise machine learning platforms to build and deploy models effectively.
• Document model logic, assumptions, and limitations to support transparency, governance, and knowledge sharing.

We'd Love to Hear From You If...

Experience

• You hold a Bachelor's or Master's degree in Data Science, Computer Science, Artificial Intelligence, Engineering, Statistics, or a related quantitative discipline.
• You have 3 to 7 years of applied experience in AI, machine learning, analytics, and Generative AI roles.
• You have hands-on experience with Generative AI solutions such as chatbots, copilots, LLM-based tools, or agentic assistants in enterprise environments.
• You have experience working in Agile delivery environments alongside product owners, Business Analysts, and technology teams.

Technical Expertise

• You have a strong understanding of the end-to-end AI lifecycle, including use case definition, feasibility assessment, evaluation, validation, and continuous improvement.
• You bring strong Generative AI expertise, including understanding model behaviour, limitations, and application frameworks, with the ability to assess solutions and guide improvements.
• You can translate business pain points into actionable AI opportunities and propose solutions that balance speed-to-market, cost, and quality.
• You are familiar with cloud-based AI platforms, preferably within the Microsoft ecosystem, such as Azure, Copilot Studio, or AI Foundry, and understand cloud and data constraints in Generative AI solution design.
• You understand regulatory considerations and can assess Generative AI use cases in regulated enterprise environments.

Ways of Working

• You are comfortable supporting iterative delivery, UAT cycles, and sprint-based refinement with business, Business Analysts, and technology teams.
• You can clearly communicate AI capabilities, risks, and recommendations to both technical and non-technical stakeholders.
• You bring strong analytical judgment and can separate high-potential use cases from those that are not yet viable.
• You work collaboratively across business and technical teams to ensure AI solutions are grounded in real business value.