Senior Data Scientist

Join a genuinely collaborative team whose diverse expertise delivers innovative solutions to complex engineering challenges worldwide. 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. The role requires a strong foundation in data science together with practical experience operating within modern data platforms, MLOps environments and software engineering workflows.

Data Science & Analytics

• 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.

Data Product Delivery

• 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.

Platform & Engineering

• 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 Jupyter Hub for model development, experimentation and deployment.
• Work with technologies to access, manage and analyse data at scale (e.g., Apache Iceberg and Trino).

Delivery Excellence

• 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.

Essential Skills & Experience

• Candidates should typically bring at least three years of relevant professional experience, although applicants with less experience and strong evidence of capability will also be considered.
• Strong Python and SQL skills, with experience developing production-quality insightful, statistical, AI and machine-learning solutions.
• Experience working with large, complex, structured and unstructured datasets, including data preparation, exploratory analysis, feature engineering and model validation.
• Practical experience with Git, Agile delivery and collaborative software-development workflows.
• Experience using distributed data-processing and MLOps tools such as Spark, PySpark, MLflow, Jupyter Hub or equivalent technologies.
• Understanding of data-product life cycles, data quality, governance, reproducibility and auditable insightful workflows.
• Ability to translate operational or business needs into insightful solutions and communicate findings clearly to technical and non-technical stakeholders.
• Effective collaboration within multidisciplinary teams, supported by strong problem-solving, documentation and critical-thinking skills.

Desirable Skills & Experience

• Advanced statistical modelling, experimental design, forecasting, anomaly detection, model optimisation or explainability.
• Deployment and operation of machine-learning models, including monitoring, drift detection and performance…