Vice President, Development Data Science

Parabilis Medicines•Cambridge, MA

11h•Onsite

About The Position

As a member of the Parabilis Medicines team, you will be a part of an organization dedicated to creating extraordinary medicines for diseases with urgent unmet needs, harnessing our proprietary peptide platform to transform treatment possibilities for patients. Parabilis is a clinical-stage biopharmaceutical company dedicated to unlocking high-impact protein targets long-considered undruggable. The company has developed a new class of stabilized, cell-penetrant alpha-helical peptides – Helicons™ – capable of modulating intracellular proteins that are inaccessible to traditional drug modalities. Headquartered in Cambridge, Mass., Parabilis is advancing a focused pipeline of multiple first-in-class therapies across both rare and common cancers. Its lead asset, zolucatetide (FOG-001), is a first-in-class, clinically validated direct inhibitor of the interaction between β-catenin and the T-cell factor (TCF) family of transcription factors, implicated in millions of cancer cases annually, including in colorectal cancer, desmoid tumors, hepatocellular carcinoma and a range of other Wnt/β-catenin-driven tumors. In Phase 1 clinical trials, zolucatetide produced the first-ever clinical evidence that it can directly inhibit this interaction, once previously considered “undruggable” despite its role across multiple cancer types. Parabilis is also advancing investigational degraders of ERG and ARON into clinical development for the treatment of prostate cancer, as well as progressing other preclinical programs. Backed by a recent $305 million Series F financing, Parabilis is entering an exciting phase of growth and execution. The VP, Development Data Science will build and lead a cross functional data centric matrix to accelerate drug development, support regulatory submissions and guide the Real-World Data (RWD) / Real-World Evidence (RWE) function at Parabilis. This role is responsible for end-to-end strategy and execution of RWD/RWE and clinical data science to inform trial design, contextualize clinical results, support biomarker and translational work, and optimize site and patient selection. The VP will partner closely with Clinical Development, Clinical Operations, Biostatistics, Translational/Computational Biology, Regulatory, and the broader Data Science & Engineering team to maximize the probability of success of our clinical programs.

Requirements

PhD, MD, PharmD, or MS in Epidemiology, Biostatistics, HEOR, Data Science, or a related quantitative field.
12–15+ years of experience in pharma/biotech, healthcare technology, or related sectors, with significant focus on RWD/RWE and clinical development; oncology experience strongly preferred.
Proven track record designing and delivering impactful RWE studies to inform clinical, regulatory, or payer decision-making.
Strong understanding of clinical trial design, biostatistics fundamentals, and translational/biomarker concepts, ideally in oncology or immuno-oncology.
Demonstrated experience building, leading, and developing high-performing quantitative teams in a matrixed environment.
Hands-on familiarity with major RWD types (EHR, claims, registries, genomic/omic datasets) and key vendors/platforms.
Proficiency with modern analytic tools and programming environments (e.g., R, Python, SQL) and with guiding development of robust, reproducible analytic workflows.
Excellent communication and influencing skills with the ability to engage effectively with clinicians, statisticians, senior leaders, and external partners.

Responsibilities

Define and own the Clinical Data Science strategy.
Serve as the senior Clinical Data Science leader and primary point of contact for RWD/RWE across programs aligned with portfolio and corporate objectives.
Represent Clinical Data Science on cross-functional governance and portfolio forums.
Design and oversee RWE studies to contextualize clinical trials, including synthetic/external control arms, patient matching, and trial emulation.
Use RWD to inform clinical trial design (eligibility criteria, endpoints, enrichment strategies), dose/regimen selection, and go/no-go decisions.
Contribute data science insights for feasibility and site selection, including epidemiology, patient journeys, referral patterns, and diversity/access.
Integrate RWD with internal clinical and biomarker data to support forward and reverse translation, target and indication strategy, and mechanism-of-resistance analyses.
Establish and maintain best practices in causal inference, comparative effectiveness, and time-to-event modeling relevant to RWE in oncology.
Identify, evaluate, and prioritize RWD sources (EHR, claims, registries, genomic/omic datasets, imaging, PROs) to support current and future clinical needs.
Lead collaboration and contracting with RWD providers and academic/industry partners in coordination with Commercial, Business Development, Legal, and Procurement.
Partner with Engineering and Data Science to define requirements for secure, scalable, compliant RWD infrastructure and analytic environments.
Ensure data quality, interoperability, and governance suitable for regulatory-grade analyses.
Build and lead a high-performing Clinical Data Science team (e.g., RWE epidemiologists, clinical data scientists, etc).
Set clear objectives, provide regular feedback, and support growth and succession planning.
Foster a culture of scientific rigor, transparency, and effective collaboration with Clinical Development, Biostatistics, and other partners.
Work in an integrated way with Clinical and Development teams to define key questions and translate them into analytic and RWE plans.
Communicate complex methods, assumptions, and results clearly to clinical, technical, and executive stakeholders.
Support regulatory and payer/Health Technology Assessment interactions where RWE and clinical analytics contribute to program strategy (e.g., briefing documents, responses, meeting preparation).
Collaborate with Translational and Computational Biology to align biomarker and patient selection strategies with integrated clinical and RWD analytics.
Ensure RWD/RWE activities meet regulatory, ethical, and privacy standards (e.g., GCP, data de-identification and privacy requirements).
Implement documentation, reproducibility, and quality standards for Clinical Data Science analyses.
Monitor evolving external expectations for use of RWE in regulatory and payer settings and adapt internal practices accordingly.