Real World Evidence Rwe

Real World Evidence (RWE) work involves analysing data about how medicines and medical devices actually perform once they are used by patients outside clinical trials - drawing on GP and hospital records, insurance and claims databases, patient registries, prescription data and patient surveys. People in these roles design and run studies on that data, then turn the findings into reports, publications and evidence packs used by pharmaceutical companies, regulators, NHS bodies and health technology assessment organisations such as NICE. Graduates typically join as an analyst, researcher or associate within an RWE, epidemiology, or health economics and outcomes research (HEOR) team.

Starting salary: £28k–£38k
Live graduate jobs: 1 open now
Main way in: Direct entry as a graduate analyst, research assistant or…
They usually ask for: A numerate or life-science degree

Approximate graduate salary

Typically somewhere around £28,000–£38,000 to start, though this varies a lot - consultancies and London-based pharma roles tend to sit at the higher end, academic and NHS research posts at the lower end, and candidates entering with a relevant MSc or PhD often start above the graduate band.

What you'd actually do

Writing and running code (usually R, SAS, Python or SQL) against large anonymised patient datasets to count how many people have a condition, what treatments they received, and what happened to them afterwards
Cleaning and reshaping messy health data - working out which diagnosis codes count as 'heart failure', dealing with missing records, deciding how to handle patients who switch treatment or leave the database
Helping draft a study protocol and statistical analysis plan: writing down in advance exactly which patients will be included, what will be measured and how, before any results are looked at
Producing tables, charts and summary outputs for senior colleagues, then helping turn them into a client report, slide deck, conference poster or journal manuscript

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Doing literature searches and evidence reviews to see what has already been published on a drug, disease or outcome, and summarising it
Sitting in on calls with clients (often pharmaceutical medical affairs or market access teams) or internal meetings where findings and their limitations are discussed
Documenting everything - version control of code, audit trails, and quality-checking a colleague's analysis so results can be defended if challenged

How graduates get in

Direct entry as a graduate analyst, research assistant or associate at a specialist consultancy or contract research organisation (CRO) - this is the most common route. Advert titles vary: RWE analyst, epidemiologist, HEOR analyst, data scientist (health).
Graduate schemes at large pharmaceutical companies, usually under headings like 'data science', 'medical affairs', 'biometrics' or 'market access'. These exist but are a smaller and more competitive entry point than consultancy roles.
Via a relevant master's - MSc in epidemiology, medical statistics, public health, health data science, pharmacoepidemiology or health economics. A master's is very common in this field and for some employers is effectively expected, though not universally required.
From an NHS or academic research post - research assistant roles on university epidemiology or health data projects are a standard stepping stone into industry RWE.

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The NHS Scientist Training Programme (health informatics route) or the NHS Graduate Management Training Scheme (analytics specialism) lead into NHS-side analytical work that overlaps with RWE, though these are public-sector rather than industry routes.
Sideways from a general data analyst or statistician job in another sector - possible, but you will usually need to show some grounding in health data or epidemiological methods.

What employers ask for

A numerate or life-science degree: statistics, mathematics, epidemiology, public health, biomedical science, pharmacy, pharmacology, economics, data science or similar. The subject does matter here more than in many graduate jobs - you need either quantitative training or biology/medicine knowledge, and ideally some of both.
Typically a 2:1 or above, though smaller consultancies and CROs are often more interested in whether you can code and reason about data than in your classification.
Coding ability in at least one of R, SAS, Python or SQL. Many employers test this at interview or with a take-home task. SAS and R dominate in pharma; SQL matters for working with large databases.
A postgraduate qualification is common. An MSc in epidemiology, medical statistics, health data science or health economics strengthens applications considerably, and some statistician-track roles effectively require one. A PhD is normal for senior scientific roles but not needed to start.

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Some understanding of study design and its pitfalls - randomised trials versus observational data, confounding, bias - usually demonstrated through a dissertation, module or project rather than work experience.
No mandatory licence or chartered status. Statisticians may later work towards Chartered Statistician (CStat) with the Royal Statistical Society; this is optional and not a gateway to the job.

Skills that matter

Programming for data manipulation (R, SAS, Python, SQL)

Almost every study starts with extracting and reshaping a large patient dataset, and you will spend more time on that than on the final statistics.

Understanding of confounding and bias in observational data

Unlike a clinical trial, patients were not randomly assigned to treatments, so the core intellectual task is working out whether an apparent effect is real or an artefact of who got which drug.

Knowledge of health data coding systems and data sources

Defining a disease or outcome means picking the right Read, SNOMED or ICD codes from a GP or hospital database, and a sloppy code list invalidates the whole study.

Clear scientific writing

The output is almost always a written document - a protocol, a report, a manuscript or a submission to a health technology assessment body - and it has to survive scrutiny from regulators, reviewers or clients.

Explaining uncertainty to non-technical audiences

Commercial and medical colleagues often want a clean headline, and part of the job is saying plainly what the data can and cannot support without losing their attention.

Meticulousness and documentation habits

Studies get re-run, queried and audited months later, so untracked code changes or undocumented decisions cause real problems.

Putting these on a CV? We counted which skills 12,914 live graduate adverts ask for, and what to do with the answer — part of our application advice.

Where it leads

Analyst/associate (roughly the first couple of years): running analyses specified by someone else, learning the data sources, contributing sections of reports.
Senior analyst or consultant: owning a workstream, designing analyses, writing protocols, dealing directly with clients or internal stakeholders. Timing varies widely - in consultancies this can come quickly, in pharma it tends to be slower and more structured.
Manager / principal / associate director: scoping projects, managing junior staff, acting as the methodological authority on a study. In pharma the equivalent is often a lead epidemiologist or RWE scientist role.
From there the paths split: technical/scientific (principal epidemiologist, statistical science lead, often with a PhD), commercial (consultancy director or partner, business development), or in-house leadership in a pharma company's medical affairs, market access or data science function.
Common sideways moves include into clinical trial biostatistics, health economic modelling, regulatory affairs, NHS or public health analytics, health-tech and data companies, or academic research.

What people get wrong

“It is a laboratory or clinical job because it sits in the pharmaceutical industry.”

You will almost never see a patient or a lab. It is desk-based quantitative work on anonymised records, far closer to data analysis than to bench science or clinical practice.

“The hard part is the statistics.”

The statistical methods are often fairly standard. The difficult and time-consuming parts are deciding what the data actually represent, defining patient groups from imperfect coding, and arguing convincingly about whether a finding is causal.

“RWE is a softer, less rigorous version of clinical trial research.”

It is a distinct discipline with its own methods and standards, and regulators and health technology assessment bodies increasingly rely on it - for rare diseases, long-term safety and real-world cost-effectiveness, it may be the only practical evidence available.

“You need a PhD, or at minimum a medical or pharmacy background, to get in.”

Plenty of entry-level roles go to graduates with a strong quantitative degree and coding skills who pick up the clinical context on the job. A PhD matters more for senior scientific posts than for getting started.

Where this varies

The setting changes the job considerably. At a specialist consultancy or CRO you work across many clients and diseases, juggle several projects at once, and face client deadlines and billable-time pressure. In-house at a pharmaceutical company you go deeper on a small number of products and spend more time coordinating external vendors and internal stakeholders than writing code yourself. In academia or an NHS/public health analytics team the work is slower-paced, more publication-driven and usually less well paid. Job titles are also inconsistent: the same work appears as 'RWE analyst', 'epidemiologist', 'HEOR analyst', 'health data scientist' or 'outcomes researcher', so it is worth reading the responsibilities rather than filtering by title. Geographically, roles cluster around London, the South East, Cambridge, Oxford and Manchester, though remote and hybrid working is common in this field.

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General guidance about the role across the UK market, not about any specific employer. Entry routes and requirements vary — always check the individual job advert.