Lead Data Scientist

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.

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.

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.

We are committed to creating an inclusive culture where everyone feels welcome and supported. If your experience looks different from what we’ve outlined but you believe you can make a strong impact in this role, we’d love to hear from you.