Senior Data Scientist

• As an experienced data scientist, you will work across the data lifecycle in a fast-paced Agile environment, helping keep people, critical businesses and communication networks connected and secure
• The role includes some client-site work, with flexible and remote working options, career development, training and a competitive reward package
• The successful candidate will develop insightful, machine learning and data-driven solutions that support defensive cyber operations, mission data exploitation and operational decision-making within a secure national security environment
• Working within Agile, multidisciplinary product teams, you will collaborate with Product Owners, Business Analysts, Data Engineers, Data Visualisers and cyber specialists to transform large-scale data into trusted, operationally useful data products
• Design, develop and deploy statistical, insightful, AI and machine learning solutions to solve operational challenges
• Explore large and complex datasets to identify trends, patterns and actionable insights
• Apply appropriate machine learning, predictive analytics and data science techniques to real-world problems
• Develop and evaluate insightful models using robust performance and validation methodologies
• Communicate findings and recommendations clearly to both technical and non-technical stakeholders
• Contribute to development of innovative AI, machine learning and advanced analytics capabilities
• Ensure large datasets are managed appropriately across staging, modelling, analytics and presentation layers
• Contribute to the design and delivery of high-quality data products that support Cyber Capability requirements
• Work collaboratively with Product Owners and Business Analysts to translate user needs into insightful solutions
• Support data quality assurance, governance and reproducibility across insightful workflows
• Develop and maintain production-quality Python and SQL code
• Use Git-based source control and branch management practices across multiple deployment environments
• Work with Spark and PySpark to process and analyse large-scale datasets
• Develop and manage machine learning workflows within an MLOps environment
• Use tooling such as MLflow, VS Code or JupyterHub for model development, experimentation and deployment
• Work with technologies to access, manage and analyse data at scale (e.g., Apache Iceberg and Trino)
• Participate fully in Agile ceremonies including sprint planning, backlog refinement, demonstrations and retrospectives
• Produce high-quality technical documentation and knowledge-sharing materials
• Contribute to continuous improvement of data science and MLOps practices

The role requires a strong foundation in data science together with practical experience operating within modern data platforms, MLOps environments and software engineering workflowsExperience using distributed data-processing and MLOps tools such as Spark, PySpark, MLflow, JupyterHub or equivalent technologiesPractical experience with Git, Agile delivery and collaborative software-development workflowsCandidates should typically bring at least three years of relevant professional experience, although applicants with less experience and strong evidence of capability will also be consideredUnderstanding of data-product lifecycles, data quality, governance, reproducibility and auditable insightful workflowsStrong Python and SQL skills, with experience developing production-quality insightful, statistical, AI and machine-learning solutionsExperience working with large, complex, structured and unstructured datasets, including data preparation, exploratory analysis, feature engineering and model validationEffective collaboration within multidisciplinary teams, supported by strong problem-solving, documentation and critical-thinking skillsAbility to translate operational or business needs into insightful solutions and communicate findings clearly to technical and non-technical stakeholdersAdvanced statistical modelling, experimental design, forecasting, anomaly detection, model optimisation or explainabilityDeployment and operation of machine-learning models, including monitoring, drift detection and performance managementAwareness of modern lakehouse or enterprise data platforms and technologies such as Trino and Apache IcebergResponsible AI or retrieval-augmented generationExperience in NLP, large language models, Generative AI, deep learning, graph analyticsDelivery of data products or decision-support capabilities in secure, governed or operational environmentsStakeholder engagement, workshop facilitation and presentation of insightful findings to senior audiencesCurious, insightful and outcome-focused, with a commitment to technical excellence and continuous learningMotivated to deliver data products that create measurable user and mission impactCollaborative and comfortable working through complexity and ambiguityAble to balance innovation with operational reliability, governance and securityEvidence of relevant continuing professional developmentA degree in data science, computer science, software engineering, mathematics, statistics, physics, engineering or another quantitative discipline, or equivalent professional experienceA postgraduate qualification or recognised professional certification in data science, AI, machine learning, data engineering, cloud platforms, Agile delivery, Responsible AI or security is advantageous but not essential

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