Data Scientist (Biostatistics)

StatsJobs
London, UK
6 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Working hours
Regular working hours

Tech stack

Artificial Intelligence Automation of Tests Cloud Computing Software Quality Continuous Integration Software Debugging Package Development Process Cloud Platform System Retrieval-Augmented Generation Large Language Models Git Machine Learning Operations
+4 more
Api Design Code Restructuring Software Version Control Databricks

Job description

Ascent has recently been acquired by Acuity Analytics. This is both a significant milestone for us and a tremendous opportunity for you. Acuity Analytics is a business with a strong global reputation, an impressive client base and ambitious growth plans. We deliver deep insights and domain-led digital transformation to high-growth and heavily regulated organisations. To our customers, we bring a partnership that provides the talent, technology and capability to enhance performance and operational efficiency., We are looking for a Senior Data Scientist (Biostatistics) to support our clients in advancing their statistical modelling capabilities.

In this role, you will work closely with client-side biostatisticians and cross-functional teams to refine modelling approaches, design robust and scalable R-based processes, and support the deployment of statistical models as production-grade software on cloud infrastructure.

This is a highly collaborative and consultative position where you’ll act as a technical expert and advisor-helping clients bridge statistical science with modern engineering best practices. You’ll ensure models are reproducible, maintainable, and production-ready, while enabling teams to adopt more efficient, scalable, and AI-accelerated ways of working., * Collaborate closely with biostatisticians and cross-functional teams

  • Refine and enhance statistical modelling approaches and methodologies
  • Design and develop robust R-based workflows and packages
  • Implement best practices for testing, documentation, and CI/CD in statistical projects
  • Support the deployment of models as scalable, production-ready software on cloud platforms
  • Provide technical guidance and mentorship to improve modelling and engineering standards
  • Ensure reproducibility, maintainability, and performance of statistical solutions
  • Use AI-assisted development tools to improve delivery speed, code quality, testing, documentation, and refactoring
  • Identify practical opportunities to apply LLMs or AI-enabled tooling within modelling, analytics, engineering, or knowledge workflows

Requirements

  • Strong background in statistics, ideally with experience as a biostatistician
  • Advanced proficiency in R, including package development
  • Solid experience with version control (Git) and collaborative workflows
  • Experience with CI/CD pipelines, automated testing, and documentation practices
  • Strong understanding of reproducible research and robust statistical modelling practices
  • Experience using AI-assisted development tools to accelerate coding, testing, documentation, debugging, and technical problem solving

Technical and platform experience:

  • Experience designing scalable R-based processes for modelling workflows
  • Familiarity with deploying models or analytical workflows into production environments
  • Experience with Databricks or similar cloud-based data platforms
  • Exposure to API development or integration (nice to have)
  • Awareness of common AI and LLM integration patterns, such as API-based integration, retrieval-augmented generation, workflow automation, tool/function calling, and human-in-the-loop review

Soft skills:

  • Excellent written and verbal communication skills
  • Ability to translate complex statistical concepts into clear, practical guidance
  • Strong stakeholder engagement and advisory mindset
  • Ability to apply sound judgement when using AI tools, ensuring outputs are reviewed, validated, and appropriate for regulated environments

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