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. You can read more about flexible working here.
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
Who are we?
We’re Standard Life, a retirement specialist focused entirely on retirement savings and income. We champion the belief that everyone’s journey to and through retirement can be better, and for more than 200 years, we’ve been helping our customers plan and prepare for their financial futures.
Life today is increasingly complicated, uncertain and unpredictable. People move through different careers, face unexpected moments and navigate important choices. We offer our colleagues flexibility, trust and benefits that work for whatever life brings. In return we expect curiosity, connection, accountability and high standards. We make room for what matters - so you can bring your best, every day.
The role
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 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
We want to hire the whole version of you.
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.
Find out more about working at Standard Life
Guide for Candidates: standardlifeplc.pagetiger.com/guideforcandidates
Find or get answers from our colleagues: www.standardlifeplc.com/careers/talk-to-us
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