Head of Science

Target RWE•Research Triangle Park, NC

8h•Hybrid

About The Position

NoviSci is a data science organization with a dual mission: developing best-in-class software to support the generation of RWE and executing high-impact, tech-enabled scientific consulting and research. Overview We are currently seeking a Head of Science who will oversee scientific strategy, leading critical client work, and partnering with Product, Statistical Software Development, and Engineering to translate methods into robust product requirements, high-level architecture decisions, and clear APIs.

Requirements

PhD (or equivalent) in Epidemiology, Biostatistics, Statistics, or related field.
8+ years post-doctoral experience spanning causal inference, statistical software, and real-world data (claims/EHR/registries); track record of leading complex studies to publication or regulatory use.
Deep hands-on expertise with causal inference, including target trial emulation, MSMs/IPTW, IPCW, g-methods, longitudinal designs, and uncertainty quantification.
Proven ability to translate scientific needs into product requirements, architecture trade-offs, and API designs in partnership with engineering (R and/or Python; package development; testing; performance profiling).
Executive-level client engagement; credible with senior scientific leaders across industry, regulators, and academia.
Evidence of thought leadership (invited talks, committees, open-source, methods papers).

Nice To Haves

Experience with CMS/Medicaid data governance and multi-source linkage/tokenization.
Leadership in distributed data networks/federated analyses.
Experience with protocol/report generators and figure/table QA pipelines (e.g., Quarto/R Markdown).
Comfort engaging on front-end reporting requirements for scientific outputs.

Responsibilities

Scientific leadership & strategy
In collaboration with the President and other leaders, define the scientific vision across products and services
Translate strategy into an executable methods roadmap.
Lead methods work, including estimator choice, diagnostics, validation, and reproducibility
Represent NoviSci with senior scientific leaders at biopharma, payers, regulators, and academic partners; lead and contribute to scientific publications and presentations.
Product partnership (requirements, architecture, APIs)
Collaborate with Product, Statistical Software Development, and Engineering to:
Define scientific requirements and acceptance criteria for features (estimands, estimators, diagnostics, simulation studies, data fitness checks).
Advise on high-level architecture (module requirements, data/metadata contracts, reproducibility/audit trails, performance considerations).
Co-design API specifications (inputs/outputs, parameterization, defaults, error handling, diagnostics objects) and review PRDs/tech specs for method fidelity.
Provide reference implementations and simulation harnesses that teams use as ground truth for verification; help design CI checks and validation datasets.
Establish documentation standards for methods notes, assumptions, and user-facing guidance.
High-value science delivery
Serve as scientific lead/PI on select flagship engagements; architect designs for causal estimation of complex interventions under confounding, missingness, and dependent censoring; develop research protocols, review SAPs, code/figures, and narratives for validity and clarity.
Define data fitness and cohort construction guardrails for claims/EHR/registries (including linkage/tokenization considerations) and ensure fit-for-purpose use.
Thought partnership for clients
Act as senior scientific advisor to client teams (Epi/HEOR/Med Affairs); help mature their internal evidence pipelines and governance; evaluate platform fit and methods choices.
Guide distributed/federated analyses and clean-room process; develop reusable scientific workflows
Team building & mentorship
Supervise scientific team leads (principal investigators)
Hire, mentor, and train methodologists, scientific programmers, and research scientists; foster a rigorous, supportive, learning-oriented culture.
Uplevel engineers and PMs on causal methods; uplevel scientists on software craft (versioning, testing, CI/CD, profiling).