Director, Analytics & Insights

About Us

Norstella is a premier and critical global life sciences data and AI solutions provider dedicated to improving patient access to life-saving therapies. Norstella supports pharmaceutical and biotech companies across the full drug development lifecycle — from pipeline to patient. Our mission is to help our clients bring therapies to market faster and more efficiently, ultimately impacting patient lives.

Norstella unites market-leading brands - Citeline, Evaluate, MMIT, Panalgo, Skipta and The Dedham Group and delivers must-have answers and insights, leveraging AI, for critical strategic, clinical, and commercial decision-making. We help our clients:

• Accelerate the drug development cycle
• Assess competition and bring the right drugs to market
• Make data-driven commercial and financial decisions
• Match and recruit patients for clinical trials
• Identify and address barriers to therapies

Norstella serves most pharmaceutical and biotech companies around the world, along with regulators like the FDA, and payers. By providing critical proprietary data supporting AI-driven workflows, Norstella helps clients make decisions faster and with greater confidence. Norstella's investments in AI are transforming how data is consumed and decisions are made, disrupting inefficient legacy workflows and helping the industry become more efficient, innovative, and responsive to patient needs.

About the Role

This is a player-manager position within the Content Strategy team in Norstella Content Operations, reporting to the VP, Content Strategy. It combines hands-on strategic ownership with leadership of a small team of technical RWD specialists (two direct reports) who implement the RWD logic and analytical solutions. The role is UK based and remote.

The Director sets the direction for RWD/RWE transformational programs and projects that address key business priorities, working in close collaboration with cross-functional business units (Technology, Product, Data Science, Services), and remains directly hands-on in the strategy, evidence logic, and specification work that underpins them, focusing on:

• Data Optimisation - Drive AI-ready, quality RWE
• Data Utilisation - Maximise data utility and unlock opportunities to leverage Norstella RWD data across the business to drive value, including as the foundation for AI solutions and agentic workflows
• Thought Partnership - Spearhead data strategy, acting as the SME bridge between client value and Norstella data across both content products and AI-driven solutions
• Defensible RWE - Own the logic and standards for how real-world evidence is defined, built, and defended (cohort definitions, endpoint-based business rules, study design), ensuring outputs are clinically sound, withstand scientific and client scrutiny, and are fit to power both content products and AI solutions and agents

Responsibilities

Serve as an influential RWD/RWE Content SME and thought partner

• Advise senior leadership across Norstella
• Set the RWD/RWE strategy - direct how real-world evidence is generated, structured, and productised across all therapeutic areas, in service of the broader content strategy
• Own cross-functional stakeholder relationships and client engagements across Product, Services, Technology, and Data Science, acting as an ambassador for the value of RWE data with internal and external stakeholders and surfacing opportunities for growth and enhancement
• Represent RWD as a function across Product, Data Science, Content, and Technology - resolving trade-offs and providing clear, clinically grounded paths forward
• Own the translation of complex, data-driven RWE concepts into business-digestible value and ROI for clients, aligned with enterprise-wide priorities
• Ensure clear lines of communication with product and AI teams developing RWE roadmap items - including AI solutions and agents that consume our data - and ensure on-time delivery of data
• Report to the executive leadership team on progress, risks, and implications of strategic initiatives

Own the defensible logic and specification of our real-world evidence

• Define and govern the standards for cohort definitions, primary and secondary endpoint logic, and real-world study design across therapeutic areas (indication-agnostic)
• Translate clinical and RWD expertise into precise, buildable specifications and business rules that Data Science and Technology can implement without ambiguity
• Provide clear, clinically grounded paths forward when the science, data, and product priorities are in tension - acting as the bridge between clinical reality and what gets built and shipped
• Ensure the defensibility of RWE outputs is maintained as offerings scale, setting the evidence bar that protects scientific credibility and commercial value
• Own the evidence logic, interpretation standards, and analytic approach that turn extracted data into actionable insights and strategic recommendations - setting the direction others build to, grounded in prior hands-on data experience rather than routine extraction
• Understand the relationship between clinical reality and its imperfect representation in healthcare data - recognising artefacts introduced by coding behaviour, reimbursement processes, site-specific practice, and incomplete longitudinal capture, and determining where absence of an event in the data can and cannot reasonably be interpreted as clinical absence
• Identify when apparently valid analytical outputs are clinically implausible, and challenge logic that is technically executable but clinically unsound
• Apply observational and epidemiological judgement to solution design - critically assessing cohort construction, index dates, follow-up definitions, and endpoint selection, and recognising material sources of selection bias, information bias, confounding, censoring, missingness, and measurement error
• Ensure solutions distinguish appropriately between descriptive observation, association, and causal interpretation, and partner with specialist epidemiologists or statisticians where advanced methodological input is required
• Establish clinically justified assumptions where RWD is incomplete or ambiguous, identify appropriate proxy measures, and specify edge cases and exception handling required for robust implementation

Ownership of end-to-end enhancement of content datasets, leading capability expansion

• Spearhead identification of whitespace and structural gaps, and architect feasible, innovative, and scalable RWE enhancements to close them - including AI-driven ones
• Work with Product and AI teams to scope disease area priorities, definitions, and requirements for both content products and AI/agent use cases, thinking strategically and commercially
• Ensure content and RWE outputs are AI- and agent-ready - structured, retrievable, and semantically consistent so they can be reliably consumed by AI solutions, agentic workflows, and downstream products
• Define the clinical knowledge, decision logic, constraints, and validation requirements for agents interacting with RWD - ensuring AI-enabled outputs distinguish clearly between observed information, calculated outputs, clinical assumptions, and inferred conclusions
• Develop validation scenarios that test whether agent outputs remain clinically coherent across representative and edge-case patients, identify failure modes where automated interpretation of RWD could mislead, and help establish guardrails so AI increases scale without eroding clinical credibility or transparency
• Work with data engineering and technology teams to productionise processes at scale
• Assess whether prospective RWD sources can support intended product capabilities and use cases - evaluating longitudinality, completeness, representativeness, granularity, linkage, and clinical specificity across EHR, claims, lab, registry, and other healthcare datasets - and advise Product teams when data limitations make a proposed capability unreliable or potentially misleading
• Build reusable clinical RWD intellectual property such as: libraries of clinical definitions, cohort frameworks, phenotype logic, treatment algorithms, line-of-therapy rules, endpoint definitions, patient journey constructs, documented assumptions, edge cases, and validation scenarios
• Establish standards for documenting clinical logic, rationale, assumptions, and known limitations, with versioning and governance so reusable logic can evolve as clinical practice, available data, and product requirements change - and ensure definitions are not reused outside contexts in which they remain valid

Team management

• Align with the VP, Content Strategy on team direction and priorities in line with wider Content Strategy priorities
• Directly manage and develop a small team of technical RWD specialists (two direct reports) responsible for implementing RWD logic and analytical solutions - providing sufficient clinical context for them to understand the intent behind what they build, reviewing implementation outputs against the approved specification, and remaining close enough to the work to coach and challenge without becoming the primary data extractor or programmer
• Direct the team's technical delivery - setting priorities for the ingestion, transformation, and standardisation of claims, EHR, and registry data into analysis-ready form, and for the data profiling, quality checks, and QC review that underpin output reliability - and reviewing that work against the approved clinical specification rather than performing it
• Implement performance management structures committed to delivering excellence, ensuring clear development and delivery goals for reports and being accountable for their delivery of those goals
• Deliver implementation through the team while personally owning the strategy, evidence logic, and specifications they build to - keeping all team members aligned on business priorities and delivering to a regular cadence
• Foster leadership development, and mentor and support junior team members

Qualifications

• 5+ years' hands-on experience in RWD study design and statistical ownership, working directly with sources such as open/closed claims, lab, EMR/EHR, registries, healthcare coding systems such as ICD 10 and NDC, and drug/medication data. Fluency in US real-world data sources and their respective strengths and limits
• Strong clinical expertise is essential - the ability to understand disease and treatment pathways, interpret healthcare data within the context of real clinical practice, identify clinically implausible assumptions or outputs, and reason through ambiguity where clinical reality is only partially represented in the data - together with the demonstrated ability to translate that reasoning into cohort definitions, endpoint-based business rules, and precise, buildable specifications
• Technical credibility to direct and quality-assure the team's work - hands-on experience with statistical validation techniques, and sufficient fluency in SQL/Python and R and/or SAS to review analytical scripts and outputs, challenge the approach taken, and give substantive feedback
• Understanding of the evidence needs of biopharma stakeholders across functions such as Clinical Development, Medical Affairs, RWE, HEOR, and Market Access, and the ability to translate recurring RWD needs into scalable product capabilities
• An MD or equivalent medical qualification is highly desirable. Candidates with other relevant clinical qualifications or backgrounds - Bachelor's or Master's in Life Sciences, Pharmacy, Medical Sciences, or equivalent - will be considered where they demonstrate substantial clinical knowledge and the ability to apply clinical reasoning to real-world healthcare data
• 10+ years' experience preferably in a biopharma intelligence, business research, or life sciences consulting domain, including prior direct line management of a small technical or analytical team