Data Analyst/Health Economist

Tolley Health Economics Ltd
Buxton
5 days ago
Create job alert

We are looking for a data analyst to join our team of health economists and HTA consultants specialising in UK Health Technology Assessment (HTA) evidence preparation.

You will play an important role in contributing to health economic projects including analysing data inputs to economic models of the cost-effectiveness of new treatments. The role would include supporting the Senior Health Economists at Tolley, working with external statistical experts, analysing clinical trial and real-world data to support health economic models, and assisting the generation of evidence for HTA submissions. Typical projects may involve analysing patient-level quality of life data to estimate health state utilities for economic models, conducting survival analysis using patient-level or digitised data, and conducting treatment comparisons based on Cox regression or other methods. You may be required to support the clinical team on data analysis plans for data extracted from systematic literature reviews. Direct involvement in building or adapting economic models is also a possibility. Outside core HTA work, we support clients with evidence generation activities including vignette-based utility studies, patient preference studies and expert elicitation, as well as presenting work through conference presentations, posters and academic manuscripts.

We work in a wide variety of disease areas from oncology to rare diseases with varying levels of evidence available, and encourage our clients to plan for evidence needs early. You should have a genuine passion for exploring and staying current with quantitative data analytical approaches to advise clients on optimal and feasible approaches, as well as being able to communicate complex ideas effectively to non-technical audiences. Tolley fosters a people-centred culture and the position would suit someone who likes working in a relatively smaller company set up with a team focussed philosophy.

Location: Buxton, Derbyshire (between Manchester and Sheffield). A flexible working policy is in place with all staff spending a proportion of time in the office and at home. Depending on your level and experience, alternative arrangements for remote working can be made.

Salary: Competitive (depending on previous experience within a similar role in academia, pharma or consultancy).

Benefits: Percentage-matched, opt-in workplace pension contribution with a top three pension provider, discretionary end-of-year bonus (based on a share of the company profits), health cash plan, above average annual leave entitlement plus Bank Holidays, and Summer and Christmas team-building events.

About Tolley

Early HTA preparation ensures a smooth, efficient pathway to launch and reimbursement with activities often focused on either the evidence generation strand or the influencing of the wider environment strand through stakeholder engagement. At Tolley, we focus on the former, the evidence generation, specifically Early Scientific Advice, systematic literature reviews (SLRs), fit-for-purpose cost-effectiveness models and decision-focussed indirect treatment comparisons, and related utility, preference and cost studies. At the same time, we believe that both should be addressed fully by a company to increase the chances of a positive outcome.

At Tolley, we offer clients a high-quality, bespoke service to support their HTA activities, using our experienced in-house team that has a wealth of experience, alongside our external trusted network of experienced independent consultants. As part of a boutique team, you will have autonomy in all aspects of health economics in the projects you lead with encouragement to present your work at international conferences and in peer-reviewed journals as Tolley place emphasis on research and publication.

Responsibilities

Your key responsibility will be performing data analyses of clinical and cost-effectiveness data to inform HTA submissions of new therapies. A knowledge of statistical and health economic methods and approaches used in an HTA context would be beneficial. The role will involve contributing to economic model development and data analysis of inputs to models, and establishing and maintaining links to commercial clients, key stakeholders (including clinicians and patient advocacy groups), the academic community, and HTA bodies.

Requirements

A post-graduate degree (MSc minimum, PhD preferred) in statistics, data science, health economics with a quantitative skills/ mindset) or a related field is required. Applicants should ideally have a minimum of 1-2 years’ experience in data analysis/ health economics beyond academic training: experience in an HEOR consultancy, and/ or working with or within pharma companies, an HTA organisation, or academia in a healthcare setting would be valuable, but is not essential.

You should be proficient in at least one statistical software package (R / STATA / SAS), as well as standard Microsoft Office packages including Excel / VBA, and have excellent oral and written presentation skills. Although it is not a requirement to have worked directly in HTA, applicants should be familiar with the approaches used commonly in the economic evaluation of health technologies, including parametric survival modelling, cox regression and longitudinal analysis. Experience with indirect treatment methods such as network meta-analysis would be beneficial but is not expected. Any previous experience of presentations at conferences, advisory boards/ workshops and a record of publication of abstracts/ posters and manuscripts is also useful.

You should also be comfortable working in a smaller company set up, where the focus is on team work, but without micro-management so prepared to use your own initiative by working independently as part of the team.

How to apply

If you are interested or would like to find out more, we encourage an informal Microsoft Teams meeting so we can get to know each other, prior to you submitting your CV with an email on why you would fit this role.

Please contact the Tolley offices on +44 (0) 1298 74855 or send an email to Marie Hutchinson ().


#J-18808-Ljbffr

Related Jobs

View all jobs

Senior Data Analyst

Data Analyst

Senior Data Analyst

Data Analyst - Workforce & Planning Analytics

Data Analyst - Workforce & Planning Analytics

Data Analyst - Workforce & Planning Analytics

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.