Acuity Analytics
Senior Data Scientist (Biostatistics)

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About us 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.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
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
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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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
Why join us People are at the Heart of our Business. By investing in people, we achieve exceptional results for our clients and create new opportunities for our teams to thrive. Check out this page for more details.
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