Associate Director, Real World Data

Formation Bio•Boston, NY

3d•Hybrid

About The Position

As Associate Director of RWD Intelligence at Formation Bio, you will lead the strategy and execution of our real-world data (RWD) capabilities, building the data foundations that power drug acquisition, clinical development, and portfolio decision-making. You will own the end-to-end lifecycle of RWD: sourcing, procurement, ingestion, harmonization, quality assurance, and delivery of analysis-ready datasets to downstream consumers across Product, Data Science, Clinical Development, and Business Development. This role sits at the intersection of data engineering, data science, and drug development. You will build and maintain scalable data infrastructure (pipelines, data models, lakes/marts) while ensuring semantic interoperability across heterogeneous data sources through ontology-driven harmonization frameworks such as OMOP. You will also manage vendor relationships and data procurement, evaluating and integrating new data assets as the portfolio evolves. The ideal candidate combines deep RWD domain expertise with strong data fluency, enabling Formation Bio to treat real-world evidence as a first-class strategic asset.

Requirements

BSc or MSc in biomedical informatics, computational sciences, epidemiology, or a related quantitative field
5+ years of industry experience working directly with real-world data (EHR/EMR, claims, registries, linked biobank data) in pharma, biotech, health tech, or consulting, with at least 2+ years in people management
Strong data engineering proficiency (pipelines, ingestion frameworks, data models, data lakes/marts) combined with deep working knowledge of biological and medical ontologies (ICD, SNOMED CT, MedDRA, RxNorm, ATC) and harmonization standards, particularly OMOP CDM
Demonstrated experience with RWD procurement and vendor management: evaluating data providers, negotiating agreements, and integrating new data assets
Proven ability to deliver RWD-derived insights across multiple drug development use cases (e.g., trial design, epidemiology, comparative effectiveness, label expansion), with familiarity across the development lifecycle from target selection through post-market
Proficiency with modern AI/ML tools, including large language models, and their applications in data engineering and harmonization workflows
Strong communication skills with the ability to translate complex data infrastructure concepts for clinical, scientific, and executive audiences

Nice To Haves

PhD in biomedical informatics, epidemiology, computational biology, or a related field
Experience with large-scale biobank and genomics-linked RWD platforms (UK Biobank, FinnGen, All of Us), with a track record of building RWD infrastructure that directly influenced drug acquisition, licensing, or portfolio decisions
Familiarity with additional biomedical data modalities (scientific literature mining, -omics datasets, molecular data integration) and with data science/analytics methodologies applied to RWD (causal inference, trial simulation, propensity score methods)
Background transitioning data infrastructure from research/ad hoc to production-grade systems in regulated environments
Experience working at the intersection of data engineering, data science, and business strategy in pharma/biotech

Responsibilities

Lead the RWD Intelligence function within Data Science, owning data strategy, sourcing, and delivery of analysis-ready datasets
Architect and maintain the supporting infrastructure (pipelines, ingestion workflows, data models, lakes/marts) across EHR/EMR, claims, registries, and genomics-linked cohorts
Drive adoption and extension of harmonization frameworks (e.g., OMOP CDM) across heterogeneous data sources, leveraging AI/ML tools for entity resolution, ontology mapping, data quality monitoring, and automated harmonization
Manage RWD vendor relationships end-to-end: evaluate providers, negotiate data use agreements, broker new partnerships, and integrate acquired datasets into the platform
Partner with Data Science, Clinical Development, Business Development, and Engineering teams to define RWD use cases (trial feasibility, synthetic control arms, epidemiology, label expansion) and productize ad hoc pipelines into scalable, production-grade systems
Foster a culture of data quality rigor, documentation, and reproducibility across all RWD assets