Note: The job is a remote job and is open to candidates in USA. Norstella is a global life sciences data and AI solutions provider that supports pharmaceutical and biotech companies across the drug development lifecycle. The Director, Analytics & Insights will lead real-world data and real-world evidence strategy, oversee a small technical RWD team, and guide cross-functional development of clinically defensible, AI-ready analytics and content solutions.
Responsibilities
- Serve as an influential RWD/RWE Content SME and thought partner advising 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 tradeoffs 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 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. Own and improve the processes and tools used to execute projects effectively
- 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
Skills
- 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 ICD10 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
- 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
- Cross-functional influence - experience representing a technical team or function to product, data and commercial stakeholders, driving aligned decisions and building effective working relationships across seniority levels
- Experience supporting the development of data, analytics, content or software products in life sciences or healthcare, including building and executing the business case for new capabilities. Strong understanding of the drug development lifecycle, and of how life science data is published, captured and processed to translate real-world events into structured content
- Excellent proficiency in Microsoft Office, including SharePoint, Excel and PowerPoint, Monday.com, LucidChart, Jira, Confluence, GitHub
- Impeccable or native English verbal and written communication, with previous experience working in global teams
- 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
- Knowledge of cloud data warehouses such as Amazon Redshift and Snowflake
- Experience integrating RWD/RWE into AI solutions and agentic workflows, or building content to be AI- and agent-ready
- Exposure to outcomes research, HEOR, market access or other biopharma evidence-generation use cases, particularly where this has involved the application of real-world data
Benefits
- Medical and Prescription Drug Benefits
- Health Savings Accounts (HSA) or Flexible Spending Accounts (FSA)
- Dental & Vision Benefits
- Basic Life and AD&D Benefits
- 401k Retirement Plan with Company Match
- Company Paid Short & Long-Term Disability
- Paid Parental Leave
- Paid Time Off & Company Holidays
Company Overview
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