Cybercrime Senior Data Scientist

Within the Department for Work and Pensions (DWP), the Security Data Science team is part of the Cyber Resilience Centre (CRC), tasked with securing our public facing payment services and protecting one of the largest repositories of personal data in the UK. The Security Data Science team support this mission by building capabilities that typically enable the detection of complex attacks and prevention of security incidents, with some data scientists focussing on the prevention of complex, digitally enabled fraud attacks. These capabilities are varied, spanning analytics, automation, infrastructure, insights and understanding of complex data sets for various stakeholders.

A Cybercrime Detection Senior Data Scientist role involves leading and shaping a range of projects that combine programming skills with data analytics techniques and close stakeholder engagement. Taking direction and working closely with Cybercrime Detection Analysts, they develop and improve monitoring and investigation capabilities.

Seniors within the Security Data Science team own significant workstreams within cyber security analytics and counter-fraud projects. They are responsible for developing the sophistication and accuracy of our analytics capabilities in tackling a range of security and fraud risks. Day-to-day their skills combine those of researcher, consultant, analyst and programmer – with the ability to develop repeatable procedures in code combined with deep contextual data analysis. They drive the use of complex techniques to model, map and programmatically analyse the behaviour of complex entities such as users, devices, and attackers.

Responsibilities

• Own relationships with enabling stakeholders within Cyber Resilience Centre (CRC), identifying the right people within the CRC Teams that can enable and support our mission.
• Identification and monitoring of known security risks to DWP systems and data, and work with stakeholders to design effective responses.
• Proactive use of data science techniques, data sources and technology to discover anomalies, assess capability and analyse security data in new ways.
• Developing strong and effective working relationships with Cybercrime Detection Analysts, Security Information and Event Management (SIEM) engineers and other data analysts to ensure emerging threats identified are fully assessed in terms of their potential impact.
• Modelling, mapping, and programmatically analysing the behaviour of complex entities such as users, devices, and attackers. Extracting, transforming, and mapping high-dimensional variables to construct unique, distinguishing data profiles.
• Where appropriate, guiding the design and implementation of capabilities. Optimising data pipelines and algorithms to efficiently scale across massive datasets without compromising accuracy.
• Where appropriate, leveraging AI capability to assist with heavy lifting in the development of complex risk-based capabilities.
• Lead strands of data science and AI work. This includes identifying the specific business need, data gathering, extracting, cleaning, and integrating large-scale, multi-source datasets; determining the best tools, data and techniques to address the problems; ongoing deliverables and presentations; and influencing the business case for delivery.
• Support the design and implementation of analytics within specific products.
• Disseminate key findings to technical and non-technical audiences in a compelling way that is clearly set within a business context and exploits interactive visualisation techniques.
• Keep abreast of emerging technologies, market and industry trends and encourage adoption and best practice throughout the Data Science, Digital and Analyst community.
• Influence the wider Data Science landscape, defining methods, standards and other best practices. Make connections with peers across Government and industry in order to help make DWP a leader in Data Science.
• Ensure work adheres to key privacy, security and data protection principles, and demonstrates ethical considerations such as preventing unintended biases.
• Design experiments and rigorous validation frameworks to ensure profile uniqueness, model stability, and performance.

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