Staff Biomedical Data Manager

PathAI•Boston, MA

16d•Hybrid

About The Position

PathAI is hiring a Staff Biomedical Data Manager to lead the governance, architecture, management, and quality strategy for large-scale, multimodal clinical and translational research datasets, including whole slide imaging. The role partners with leadership in machine learning, biomedical data science, program management, Algorithm Product, and Translational Research to define dataset structures, platform capabilities, and scalable processes. As the primary technical authority for the digital pathology data domain, the position works with partners on complex integrations and sets data excellence standards across the organization. The role drives the technical roadmap for platform evolution and develops reusable data models, workflows, tooling, and processes that minimize bespoke work, improve data quality, and accelerate delivery. It also provides technical expertise for internal ML platforms and algorithm deployments, overseeing their execution, validation, and lifecycle management while maintaining data integrity from ingestion through analysis. The position sets team tooling and efficiency strategy, provides guidance on complex data challenges, and governs technical documentation, SOPs, and cross-functional training. The position is fixed-location in PathAI’s Boston, Massachusetts office; telecommuting from a home office may also be allowed. Multiple positions are available.

Requirements

Bachelor’s degree in Biomedical Informatics, Biomedical Engineering, Public Health, Epidemiology, Biostatistics, or a related field, plus three years of experience as a Staff Biomedical Data Manager or progressive experience in biomedical data management focused on data architecture, curation, and communication; experience may be gained concurrently.
Two years of experience using Python or R for large-scale data wrangling, statistical modeling, and automated reporting.
Two years of experience architecting and maintaining production-grade ETL/ELT pipelines using industry-standard tools such as Spark, Airflow, Databricks, or similar.
Two years of experience managing datasets within strict regulatory frameworks.
Two years of experience in data harmonization and standardization, specifically mapping disparate data sources to common data models.
One year of experience managing end-to-end digital pathology workflows, including ingestion, storage, and metadata extraction for whole slide images (WSI).
One year of experience utilizing CI/CD for data (GitHub Actions), data monitoring and observability tools, and environment management (Docker).
One year of experience working with medical terminology (oncology and pathology) or clinical trial structure.
One year of experience working with oncology-specific data archetypes, including TNM staging, histology codes, and biomarker status.
One year of experience working with sensitive datasets.
Alternatively, a Master’s degree and two years of experience.

Responsibilities

Partner with leadership in machine learning, biomedical data science, and program management to define how clinical and translational research datasets are governed, structured, and scaled.
Own the vision for maximizing company platform capabilities and architect improvements to the system for hosting high-velocity, multimodal histopathology data.
Serve as the primary technical authority for the digital pathology data domain, interface with partners on complex integrations, and set standards for data excellence across the organization.
Architect and own the end-to-end data management, governance, and quality strategy for large-scale, multimodal clinical programs, including whole slide imaging.
Drive the technical roadmap for data platform evolution in partnership with senior leadership, defining long-term opportunities and scalable process architecture.
Establish technical standards and best practices for data management processes to ensure consistency and scalability across products and partnerships.
Serve as lead Subject Matter Expert for digitalization of clinical data, translating complex partner needs into scalable technical requirements for platform teams.
Lead the design and implementation of scalable, reusable data models and workflows for novel projects, minimizing bespoke work.
Serve as lead technical expert on internal ML platforms and design solutions for complex deployment, validation, and data integration challenges.
Oversee and automate execution, validation, and lifecycle management of major algorithm deployments, ensuring data integrity from ingestion to analysis.
Engineer solutions and tooling to abstract data workflows, improve data quality, and reduce time to delivery.
Define the long-term strategy for team tooling and efficiency, including identifying, vetting, and championing new technologies.
Provide technical leadership and guidance on complex data-related barriers while fostering innovation and proactive problem solving.
Create and govern standards for technical documentation, SOPs, and cross-functional training.