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Regeneron Pharmaceuticals

Develops biopharmaceutical therapies for serious diseases

Associate Director Solutions Partner - Scientific Data Product Owner

Technical Product ManagementConfirmed live in the last 24 hours
Full-timeTarrytown, NY, USA$157.2k - $256.6k /yr

About the Job

Requirements

  • A BS/BA degree and/or MS degree in a related field is required.
  • 10+ years of progressive experience managing scientific laboratory data, ideally within the biopharmaceutical or life sciences industry.
  • A strong scientific background in chemistry, biology, molecular biology, biochemistry, immunology, pharmacology, or a related discipline, with the ability to understand the meaning and context of the data being modelled.
  • Hands-on experience with data modelling and understanding how to connect data across multiple source systems.
  • Ability to drive diverse stakeholders to alignment on desired outcomes and influence others at multiple levels without direct authority.
  • Working knowledge of data engineering concepts, including data pipelines, ETL/ELT, and data transformation, sufficient to define requirements for and collaborate effectively with data engineers.
  • Strong SQL skills and proficiency in a primary Databricks language, such as SQL or Python/PySpark.
  • Familiarity with transactional lab informatics systems such as ELN, LIMS, and instrument or analytical data platforms.
  • Product ownership or product management experience, including roadmap definition, backlog prioritization, and stakeholder management.
  • Strong communication skills and ability to bridge scientific, technical, and analytics audiences.
  • An advanced degree in a biology discipline is required.

Responsibilities

  • Define and drive the holistic data strategy for modelling and managing connected scientific data in the data lake, ensuring data from transactional lab informatics systems is harmonized, contextualized, and connected.
  • Partner with data engineering to define and shape ingestion, transformation, and integration pipelines that move data from transactional lab informatics systems into the data lake and turn it into connected, production-grade data products.
  • Own the product vision, roadmap, and backlog for scientific data products, prioritizing based on scientific value, downstream demand, and organizational impact.
  • Design and govern data models that capture relationships across scientific entities, including compounds, biologics, samples, assays, experiments, results, instruments, and study metadata.
  • Translate scientific, Digital & Technology, and analytics stakeholder needs into clear data product requirements, acceptance criteria, and delivery plans, and drive outcomes aligned with stakeholders and corporate Digital and Transformation strategy.
  • Partner with Digital & Technology teams consuming connected data products from the data lake for analysis and reporting, ensuring products meet integration, quality, and access requirements.
  • Partner with data scientists seeking connected scientific datasets, ensuring datasets are analytics-ready, well-documented, and fit for modelling and AI/ML use cases.
  • Establish and uphold data quality, governance, lineage, metadata, and FAIR principles across scientific data products.
  • Conceive, elicit, and champion AI-driven approaches for searching and discovering structured scientific data, improving how users find and connect relevant datasets.
  • Act as the primary point of contact and advocate for scientific data products, gathering feedback and continuously improving usability, coverage, and value.

Desired Qualifications

  • A PhD in molecular biology, biochemistry, genetics, or immunology is preferred.
  • Hands-on experience with Databricks or a comparable lakehouse platform for managing and delivering data products.
  • Experience leveraging AI to search, discover, and interrogate structured data.
  • Experience with knowledge graphs, ontologies, controlled vocabularies, or semantic data models for connecting scientific entities.
  • Understanding of data lake/lakehouse architectures and modern data engineering practices.
  • Experience working with data scientists and analytics teams to deliver analytics-ready datasets.
  • Familiarity with data governance frameworks and FAIR data principles in regulated and unregulated environments.
  • Experience with metadata management, data cataloging, and data lineage tooling.
  • Experience with AI/ML workflows and the data requirements that support them.
  • Awareness of regulatory and compliance considerations relevant to pharmaceutical research and development data.

About the Company

Regeneron Pharmaceuticals

Develops biopharmaceutical therapies for serious diseases

Regeneron Pharmaceuticals develops and commercializes medicines for serious diseases, with a focus on cancer, eye diseases, allergic and inflammatory diseases, and infectious diseases. Its products come from using proprietary research capabilities and technologies to discover and develop therapies, often in collaboration with academic, research, and industry partners. Revenue comes from selling approved medicines and from licensing its technologies and entering co-development and co-commercialization partnerships. The company works with healthcare providers and regulators to ensure treatment delivery and safety. Its goal is to improve patient outcomes by bringing life-transforming medicines to market through rigorous R&D and strategic collaborations, maintaining leadership in biopharmaceutical innovation.

Company size
10,001+
Company stage
IPO
Headquarters
Town of Mount Pleasant, New York
Founded
1988

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