Data Science Lead in Edinburgh

Job Type: Perm

Location: This role will be based in our Edinburgh office.

Flexible working: All of our roles are open to part-time, job-share and other types of flexibility. We will discuss what is important to you and balancing this with business requirements during the recruitment process.

Salary and benefits: Up to £75,000 plus discretionary bonus, private medical cover, 38 days annual leave, excellent pension, 12x salary life assurance, career breaks, income protection, 3x volunteering days and much more.

Closing Date: 13th May

We have an exciting opportunity to join the Pensions & Savings business as a Lead Data Scientist within Strategy and Transformation. As a Lead Data Scientist, you will take technical and delivery ownership for complex, high‑impact AI, machine learning and analytic initiatives across the business. You will lead the end‑to‑end design, development, deployment and ongoing optimisation of advanced analytics and ML solutions, ensuring they deliver measurable business outcomes and meet regulatory, risk and governance standards.

You will act as a senior technical authority and delivery lead, working closely with stakeholders across product, operations, technology, risk and transformation. You will play a key role in establishing modern data science and MLOps practices, enabling the team to scale analytics and AI safely, reliably and at pace. This is a hands-on role that requires strong technical depth, delivery focus, and the ability to navigate complex organisational structures, influence senior stakeholders, and translate business problems into production‑grade AI solutions.

Key responsibilities

• Lead the end‑to‑end delivery of AI / ML and analytic initiatives, from problem definition and solution design through to deployment, monitoring and continuous improvement.
• Design and build production‑grade machine learning solutions, applying appropriate modelling techniques (supervised, unsupervised, NLP, optimisation) aligned to business needs.
• Champion and apply MLOps best practice, including:
• Model versioning, testing and validation
• CI/CD pipelines using Azure DevOps
• Automated deployment, monitoring, drift detection and retraining
• Documentation, audit trails and governance artefacts
• Act as a technical design authority for data science solutions, ensuring consistency with enterprise architecture, security, risk and compliance expectations.
• Work closely with data engineering, platform and cloud teams to ensure models are scalable, resilient and operationally supported.
• Engage senior stakeholders to:
• Frame business problems effectively
• Manage expectations and trade‑offs
• Communicate insight, limitations and outcomes clearly
• Influence decision‑making using data and evidence
• Operate comfortably within a complex organisational environment, balancing priorities across multiple teams, initiatives and governance forums.
• Set standards and contribute to the development of data science ways of working, tooling, templates and best practice.
• Provide technical leadership and mentoring to Data Scientists, supporting capability uplift and knowledge sharing across the team.
• Ensure all solutions comply with relevant risk, data governance, model risk and regulatory requirements, maintaining robust evidence and auditability.

What We’re Looking For

Essential experience

• Extensive experience delivering end‑to‑end data science / machine learning solutions in a production environment.
• Strong programming skills in Python and SQL, with experience working with large‑scale datasets (e.g. Spark, distributed compute).
• Hands‑on experience with Azure DevOps (or equivalent) for source control, pipelines and deployment automation.
• Solid software engineering discipline, including Git‑based workflows and code reviews, modular, testable code.
• Experience working with cloud‑based data platforms (data lakes, warehouses) and partnering closely with data engineering teams.
• Strong stakeholder management skills, with the ability to explain complex technical concepts to non‑technical audiences and influence senior decision‑makers.

Desirable experience

• Experience operating in highly regulated environments (e.g. financial services).
• Proven experience implementing MLOps practices, including model lifecycle management, CI/CD and monitoring.
• Familiarity with model governance, validation and audit requirements.
• Experience contributing to enterprise‑wide analytics or AI platforms.
• Coaching or technical leadership experience within data science teams.