Real World Evidence has moved from the periphery of HealthTech to the centre of it.
Regulators are accepting RWE in support of label expansions and post-market surveillance. Payers are using it as the basis for coverage and reimbursement decisions. Health systems are demanding it as part of procurement processes. And investors are increasingly treating a credible RWE strategy as a signal of commercial maturity.
The problem is that the talent required to build and run an RWE function is in short supply. This piece covers what a strong RWE team looks like, which roles are hardest to fill, and what HealthTech hiring leaders should know before they go to market.
Why RWE Talent Is So Scarce
Real World Evidence sits at a specific intersection of epidemiology, health data science, regulatory science, and clinical research methodology. Candidates who are genuinely strong across that combination are rare. Most have deep expertise in one or two areas and are learning the others on the job.
The academic pipeline is limited. RWE as a formal discipline is relatively recent, and the graduate programmes that train specifically for it (rather than for clinical research or epidemiology more broadly) are still small. Most experienced RWE professionals came up through pharma, CROs, or health economics consultancies, and their transition into HealthTech is relatively recent.
Demand has outpaced supply. The growth of regulatory acceptance for RWE, combined with the explosion of HealthTech companies seeking evidence-based market access, has created demand that the existing talent pool simply cannot meet at pace. Day rates and salaries for senior RWE professionals have risen sharply as a result.
What a Strong RWE Team Looks Like
The composition of an RWE team varies by company stage and the specific evidence questions being pursued. At a minimum, a functioning RWE capability requires three types of expertise.
Epidemiological and Study Design Expertise
The ability to design studies that will hold up to regulatory and payer scrutiny is foundational. This typically requires a candidate with formal training in epidemiology or a related discipline, direct experience designing observational studies using real-world data sources, and familiarity with the methodological standards expected by FDA, EMA, or NICE depending on the target market.
Health Data Science and Analytics
RWE depends on the ability to work with large, messy, heterogeneous health datasets (electronic health records, claims data, patient registries, wearable data) and extract defensible analytical outputs. This requires health data scientists who understand both the statistical methods appropriate for real-world data (propensity scoring, instrumental variable analysis, target trial emulation) and the practical realities of working with incomplete or biased datasets.
Regulatory and Market Access Interface
RWE evidence packages are only valuable if they are positioned correctly with the relevant regulatory or payer audience. Someone who understands how FDA’s Real-World Evidence Programme, EMA’s guidance on RWE in regulatory decision-making, or NICE’s evidence standards apply to the specific evidence being generated is essential for ensuring that the analytical work actually lands.
The Roles That Are Hardest to Fill
Director of Real World Evidence.
Senior RWE leaders with a track record of delivering evidence packages that have influenced regulatory or payer decisions are exceptionally scarce. Most have been in post for several years and are not actively looking. Finding them requires proactive search rather than reactive advertising.
RWE Data Scientists with Health Economics Overlap.
The combination of health data science skills and health economics literacy is narrow. Candidates who can model clinical outcomes and translate them into cost-effectiveness arguments are in particularly high demand.
RWE Programme Managers.
The operational management of multi-study RWE programmes is a distinct skill set that is often undervalued in hiring briefs. Strong RWE programme managers with experience coordinating across data access, ethics, regulatory timelines, and analytical delivery are hard to find and easy to lose to better-organised competitors.
How to Hire Better in RWE
The hiring leaders are successfully recruiting RWE talent by:
Hiring for learning agility alongside technical expertise.
Given how fast RWE methodology is evolving (particularly around digital biomarkers, real-time data, and AI-assisted analysis), candidates who have demonstrated the ability to adapt their analytical frameworks matter more than those with a static but deep technical skill set.
Being realistic about what a single hire can cover.
Most HealthTech companies try to hire one person to own everything across study design, analytics, and regulatory interface. That profile is vanishingly rare. A more successful approach is to hire for the most critical gap first and build the function over time.
Looking beyond traditional pharma pipelines.
Some of the strongest RWE talent is coming from academic health data research, NHS analytics functions, and health tech companies that built serious data capabilities early. These candidates bring methodological rigour and real-world data fluency that is sometimes stronger than candidates from large CROs.
How Storm3 Supports RWE Hiring
Storm3’s Real World Evidence recruitment team works with BioTech and HealthTech companies at every stage of building their RWE capability.
Our network spans Clinical Trials Tech and HealthTech Data & Analytics talent, and we understand the specific combination of skills that a genuine RWE hire requires.
We work on both permanent searches and contract engagements for companies that need RWE expertise to support a specific regulatory submission or payer dossier before building a permanent function.
Submit your vacancy and our team will be in touch.
The post The Hiring Gap HealthTech Companies Can’t Ignore appeared first on Storm3.
