Job Description
Overview
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.
Your role
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 JupyterHub 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.
About you
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, JupyterHub or equivalent technologies.
Understanding of data-product lifecycles, data quality, governance, reproducibility and auditable insightful workflows.
Ability to…