Director, Advisory Biostatistics at Avalere Health
Avalere Health · Washington, United States Of America · Remote
What you'll do
- Client delivery & methodological leadership (target ~40% billable)
- Serve as an advanced analytics project architect for practice (Evidence & Strategy, Policy, and Market Access) engagements requiring biostatistics and complex methodology.
- Lead cross-practice review for power/sample size calculations, statistical design choices, and sensitivity analysis planning for high-stakes studies.
- Provide senior technical leadership on study design and analysis execution, including protocol development, SAP authorship/review, statistical modeling, interpretation, and defensible write-up for client and publication audiences.
- Act as an internal challenger to pressure-test assumptions, scopes, and deliverables, improving rigor, feasibility, and credibility before work is finalized.
- Architect validation and QC approaches for complex analytic tools (e.g., large Excel models or web-based analytic applications), including testing strategy, versioning expectations, and controlled change processes.
- Privacy certification, anonymization, and governance leadership
- Lead Avalere’s approach to privacy certification/anonymization in analytics workflows, including documentation expectations and quality controls.
- Develop and maintain statistical QC frameworks aligned to privacy-preserving use of sensitive healthcare data.
- Provide practical guidance on de-identification strategies (e.g., Safe Harbor vs Expert Determination) and analytics design choices that reduce re-identification risk and improve defensibility.
- Business development, feasibility, and proposal support (target ~20%)
- Manage and triage incoming data/analytics requests; advise on conceptual feasibility, data readiness, methodological fit, timelines, and resourcing.
- Lead or support a cross-practice feasibility review forum to standardize intake, solutioning, and risk/assumption documentation.
- Review and approve methodological sections of proposals and SOWs, including analytics scope, endpoints, data language, QC expectations, and deliverable feasibility.
- Standards, SOPs, and reusable IP (target ~30%)
- Develop and maintain SAP templates, QC SOPs, peer review checklists, and analysis standards to improve consistency and reduce publication risk across teams.
- Establish documentation standards for reproducible research and auditability (e.g., protocol/SAP structure, table shells, QC logs, data dictionaries, lineage, and analysis traceability).
- Serve as a methodological evaluator/challenger for in-flight tools and methodologies, recommending improvements to drive scalability, efficiency, and quality.
- Mentorship, enablement, and cross-functional partnership
- Mentor analysts and managers; elevate statistical storytelling, defensible methods, and QC discipline across project teams.
- Partner with data engineering and AI/technology collaborators on evaluation design for analytics tools and accelerators (focus on fit-for-purpose use, validity, and controlled deployment rather than model-building ownership).
About you
- Education
- PhD or MS in Biostatistics, Statistics, Epidemiology, or a related quantitative field (PhD preferred).
- Experience
- Significant experience (typically 5+ years) applying biostatistics in US healthcare/RWE settings, including consulting or client-service delivery.
- Demonstrated ability to produce and/or oversee publication-grade analytics outputs, including rigorous study design, reproducibility, QC, and clear interpretation for non-technical stakeholders.
- Experience leading or materially contributing to privacy/anonymization approaches and associated governance/documentation for healthcare data analytics.
- Proven success reviewing and shaping analytical scopes for proposals/SOWs and improving feasibility, risk management, and delivery quality across teams.
- Technical and methodological capability
- Advanced proficiency in R and/or Python, plus strong SQL (SAS familiarity a plus).
- Depth in methods commonly required for RWE/HEOR, such as:
- GLM/GLMM, survival/time-to-event, longitudinal/mixed models
- Causal inference approaches (e.g., propensity methods) and quasi-experimental designs
- Missing data strategies and sensitivity analyses
- Power/sample size and study feasibility planning
- Ability to architect validation and QC for complex analytic tools (Excel/web), including testing strategy and controlled change management.
- Consulting and leadership competencies
- Strong consulting judgment and stakeholder management; ability to translate technical conclusions into decision-ready narratives.
- Comfortable operating as a cross-practice reviewer and standard-setter, constructively challenging assumptions and raising the bar on rigor.