Senior Data Scientist (Biostatistics)

Acuity Analytics; Lisboa; On-site; EUR 50,000 - 70,000; Full time

Job summary

Acuity Analytics is seeking a Senior Data Scientist (Biostatistics) to enhance client statistical modelling capabilities. You will collaborate with biostatisticians, design R-based processes, and act as an advisor on bridging statistical science with engineering best practices.

The ideal candidate has a robust statistical background, proficiency in R, and experience with CI/CD and AI-assisted tools. This role promises the opportunity to support the deployment of scalable models on cloud platforms in a collaborative environment.

Qualifications

• Advanced proficiency in R, including package development.
• Strong understanding of statistical modelling practices.
• Excellent written and verbal communication skills.

Responsibilities

• Collaborate closely with biostatisticians and cross-functional teams.
• Refine and enhance statistical modelling approaches.
• Design and develop robust R-based workflows.

Skills

Strong background in statistics

Advanced proficiency in R

Solid experience with version control (Git)

Experience with CI/CD pipelines

Understanding of reproducible research

Experience using AI-assisted development tools

Tools

Databricks

API development

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.

About the role

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.

Skills and Experience required

Core skills:

• 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
• Excellent written and verbal communication skillsAbility 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

What you will do

• 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