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AtkinsRéalis

Engineering services for infrastructure and energy

Senior Data Scientist

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • Candidates should typically have 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 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 workflows.
  • Ability to translate operational or business needs into solutions and communicate findings clearly to technical and non-technical stakeholders.
  • Effective collaboration within multidisciplinary teams, supported by problem-solving, documentation, and critical-thinking skills.
  • A degree in data science, computer science, software engineering, mathematics, statistics, physics, engineering, or another quantitative discipline, or equivalent professional experience.
  • Evidence of relevant continuing professional development.

Responsibilities

  • Design, develop, and deploy statistical, 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 models using robust performance and validation methodologies.
  • Communicate findings and recommendations to technical and non-technical stakeholders.
  • Contribute to development of 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 data products that support Cyber Capability requirements.
  • Collaborate with Product Owners and Business Analysts to translate user needs into solutions.
  • Support data quality assurance, governance, and reproducibility across 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 tools such as MLflow, VS Code, or JupyterHub for model development, experimentation, and deployment.
  • Work with technologies such as Apache Iceberg and Trino to access, manage, and analyse data at scale.
  • Participate in Agile ceremonies, including sprint planning, backlog refinement, demonstrations, and retrospectives.
  • Produce technical documentation and knowledge-sharing materials.
  • Contribute to continuous improvement of data science and MLOps practices.

Desired Qualifications

  • 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.
  • Experience in NLP, large language models, Generative AI, deep learning, or graph analytics.
  • Experience with Responsible AI or retrieval-augmented generation.
  • Delivery of data products or decision-support capabilities in secure, governed, or operational environments.
  • Stakeholder engagement, workshop facilitation, and presentation of findings to senior audiences.
  • 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.

About the Company

AtkinsRéalis

Engineering services for infrastructure and energy

AtkinsRéalis provides engineering services for infrastructure and nuclear projects, connecting people, data, and technology to plan, design, build, and manage systems worldwide. It delivers integrated services through consulting, design, engineering, and project management, using a global team and data-driven collaboration to move projects from concept to commissioning. The company’s global reach and end-to-end capability across conventional engineering and nuclear sectors enable coordinated delivery across multiple disciplines. Its goal is to transform infrastructure and energy systems worldwide by delivering safe, reliable, and efficient engineering solutions.

Company size
10,001+
Company stage
IPO
Headquarters
Epsom, United Kingdom
Founded
1938

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