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AtkinsRéalis
AtkinsRéalis

Senior Data Scientist

Job summary
Permanent contract
London
Salary: Not specified
Experience: > 3 years
Skills & expertise
AI and machine learning
Presentation skills
Communication skills
Machine learning
Generative AI
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AtkinsRéalis
AtkinsRéalis

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Questions and answers about the job

The position

Job description

  • 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

Preferred experience

  • The role requires a strong foundation in data science together with practical experience operating within modern data platforms, MLOps environments and software engineering workflows
  • Experience using distributed data-processing and MLOps tools such as Spark, PySpark, MLflow, JupyterHub or equivalent technologies
  • Practical experience with Git, Agile delivery and collaborative software-development workflows
  • 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
  • Understanding of data-product lifecycles, data quality, governance, reproducibility and auditable insightful workflows
  • 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
  • Effective collaboration within multidisciplinary teams, supported by strong problem-solving, documentation and critical-thinking skills
  • Ability to translate operational or business needs into insightful solutions and communicate findings clearly to technical and non-technical stakeholders
  • 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 management
  • Awareness of modern lakehouse or enterprise data platforms and technologies such as Trino and Apache Iceberg
  • Responsible AI or retrieval-augmented generation
  • Experience in NLP, large language models, Generative AI, deep learning, graph analytics
  • Delivery of data products or decision-support capabilities in secure, governed or operational environments
  • Stakeholder engagement, workshop facilitation and presentation of insightful findings to senior audiences
  • Curious, insightful and outcome-focused, with a commitment to technical excellence and continuous learning
  • Motivated to deliver data products that create measurable user and mission impact
  • Collaborative and comfortable working through complexity and ambiguity
  • Able to balance innovation with operational reliability, governance and security
  • Evidence of relevant continuing professional development
  • A degree in data science, computer science, software engineering, mathematics, statistics, physics, engineering or another quantitative discipline, or equivalent professional experience
  • A 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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