Department for Work and Pensions (DWP)
Lead Data Scientist
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Job description
The Grade 6 Lead Security Data Scientist is responsible for the strategic direction, capability, and impact of DWP's Security Data Science team. This means owning the team's vision, setting technical standards, and ensuring the team's work is tightly aligned to DWP's security objectives: not just delivering machine learning, AI, and analytics, but making the case for data science at senior levels across the Cyber Resilience Centre and its parent group, the Security & Data Protection (S&DP) directorate.
This is primarily a leadership role. Day-to-day you will be setting direction, developing people, and maintaining the stakeholder relationships that generate valuable work. You will also contribute technically: reviewing and shaping methodology, making sound judgements on complex problems, and remaining close enough to the work to credibly develop others. Expect around 20% of your time to be spent on direct technical contribution.
The right candidate is a strong practitioner who has grown into leadership: someone with a track record of taking data science or security analytics workstreams from R&D through to production, and who now gets more satisfaction from building a team's capability than from solving problems alone.
Responsibilities
Leadership and strategy
- Set the strategic direction for Security Data Science within DWP, ensuring the work of the team advances the organisation's security objectives.
- Own the team's delivery standards, defining what good looks like across methodology, tooling, and outputs, and holding the team to it.
- Drive resourcing decisions, ensuring the right skills mix across data science, AI engineering, performance analysis, and other roles.
People and capability
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- Act as a player coach and technical mentor, inspiring curiosity and creativity in team members and supporting their professional development.
- Own the team's professional development: running learning programmes, identifying skill gaps, and evaluating emerging techniques and tooling for adoption in the DWP security context.
Stakeholders and partnerships
- Build peer and senior relationships across the business, including security operations and policy, to create a pipeline of valuable work for the team to deliver.
- Build and maintain strong relationships with data science peers across DWP and with adjacent technical communities within CRC and S&DP including data engineers, performance analysts, and security architects, encouraging collaboration, reuse, and avoiding duplication of effort.
- Represent DWP Security Data Science externally, including at cross-government forums and with bodies such as NCSC, GC3, and other government departments, to share expertise and shape the wider community of practice.
Technical delivery
- Directly manage the quality and consistency of machine learning, AI, and analytics-driven initiatives that deliver critical strategic security objectives.
- Define and lead initiatives to provide appropriate platforms that support modern Data Science workflows, integrated directly into automated security pipelines.
- Take responsibility for the full product delivery lifecycle of analytics capabilities, including overseeing discovery, development, deployment, monitoring, maintenance, and continual improvement of live capabilities.
- Champion user research within the team, designing and managing processes to understand the needs of operational analysts, SOC teams, and other internal users to ensure data science products deliver real impact.
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- Develop and own the Security Data Science ethical framework, overseeing compliance with data ethics standards and legislation, developing a data ethics culture within the team, and ensuring ethics is applied appropriately across all analytics capabilities and programmes.
Person specification
Essential Criteria
Experience
- A strong practitioner background in data science or machine learning, with in-depth knowledge of at least one specialism (e.g. NLP, graph analytics, time-series modelling, deep learning) and the ability to make sound technical judgements across the full stack.
- Proven ability to lead, grow and develop high-performing technical teams, including setting technical direction, managing performance, and building capability in others.
- Experience of building AI applications, including Retrieval Augmented Generation, prompt engineering, observability and evaluation.
- Excellent stakeholder management with the ability to translate complex technical concepts for non-technical audiences and build momentum for change at senior levels.
- Experience of strategy and delivery in a large public or private sector organisation, with a track record of taking data science workstreams from conception through to production.
- Expertise in cyber security data science, including ML-based detection, anomaly detection, or behavioural analytics capabilities in a security or fraud context.
This role is a hybrid of specialisms, drawing from both data science and cybersecurity. We encourage you to apply if you aren’t sure if you fit all the criteria.
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