Health Outcomes Research Manager

Pharmiweb
Birmingham
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Engineer

Data Scientist

Data Scientist

Research Assistant in Machine Learning for Clinical Trials

Senior Machine Learning Research Engineer

Principal Machine Learning Scientist - Applied Research (UK Remote)

Role description

Health Outcomes Research Manager - Exciting new opportunity in Health Outcomes Research and Market Access

Role: Health Outcomes Research Manager
Therapy Area: Pharmaceuticals - Cardiorenal Metabolic, Oncology, Immunology and Mental Health
Package: Attractive and negotiable daily rate.
Location: Remote - Home Based UK -2 days a week in Berkshire head office
Fixed Term Rolling Contract

This is an excellent opportunity to join a leading pharmaceutical company as an Outcomes Research Manager

In this role you provide robust relevant real world data and epidemiological reasoning in order to drive patient access solutions and aid evidence based decision making across the Prescription Medicine (PM) portfolio, and increase patient access. In addition, you will raise awareness of disease and/or product specific outcomes related to the portfolio and contribute to the publication of outcomes research studies to raise awareness of disease and/or product specific outcomes. The role also includes facilitation of the evidence base supporting HTA and the payer value proposition.

Key responsibilities:
Support the production of successful epidemiological and real-world data studies in order to generate insights about the disease, burden and related treatment landscape.
Assist in the writing of protocols, data analysis and the production of reports and manuscripts
Remains up to date on external developments w.r.t RWE methodologies accepted by Health Technology Assessment agencies and related stakeholders in relevant therapeutic areas.
Responsibilities include continuous monitoring of literature for trends in outcomes research/ epidemiology / biostatistics and stakeholder engagement to better understand acceptability of OR methods for facilitation of the payer value proposition and HTAs in the five nations.
Translate patient access strategy into robust outcomes research strategies and client studies.
Proactively communicate actionable conclusions, recommendations, and payer value messages.
Validate OR approach and results with external stakeholders.

The client is a leading global pharmaceutical company, dedicated to the discovery, development, manufacturing and marketing of innovative health care products. They have a reputation for providing effective products for the treatment of heart diseases, metabolic diseases, cancer, lung diseases, skin diseases, mental disorders and retinal diseases.

Qualifications

Degree or equivalent qualification in mathematics, statistics, epidemiology, data science or related discipline
Relevant post-graduate qualification, e.g., MSc/PhD in Epidemiology or related discipline

Person experience required

In-depth knowledge of OR methods / epidemiological methods / and medical statistics applied to epidemiology / RWD
Proven knowledge in the management of real world data and methods used to organise and analyse large sets of data
Knowledge and experience of running OR studies, from proposal writing to publication of results
Knowledge of statistical methods applied to health economics would be advantageous
Good understanding of CPRD (or equivalent anonymized patient-level database) and the UK health system would be advantageous
Good understanding of SQL and R would be advantageous

To Apply
If you are suitable for this position, please send a copy of your CV. Alternatively call the recruitment team at Chemistree Solutions Ltd. Chemistree is a pharmaceutical and healthcare recruitment specialist.

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.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.