Associate Director Solutions Partner, Scientific Data Product Owner

Regeneron

Confirmed live today High trust
Hybrid

Quick summary

Work type
Hybrid
Location
Tarrytown, NY
Salary
$157,200–$256,600 / yr
Employment
Full-time
Posted
43 days ago
Freshness
Confirmed live today
Closes
Nov 30, 2026

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $223k
This role $207k
$144k $282k
below market most similar roles pay here above market

This role pays less than 59% of similar roles. Most pay $186,281–$259,447 — the blue band above. At the midpoint, this role pays about $207k versus about $223k for comparable roles.

Based on 240 similar postings.

Employer

About Regeneron

Regeneron is a leading science-driven biotechnology company that invents, develops, and commercializes life-transforming medicines for serious diseases.

Regeneron currently has 73 open roles on FindRole.

Listed pay typically runs $134,400–$241,200 across 46 roles with salary data.

Most-posted roles

View all roles at Regeneron

At a glance

TL;DR · Associate Director Solutions Partner, Scientific Data Product Owner

The Associate Director Solutions Partner (Scientific Data Product Owner) leads the holistic data strategy for modeling and managing research and preclinical development scientific data within an enterprise data lake. This role involves transforming siloed transactional records from lab informatics systems into connected, reusable data products. You will own the product vision, roadmap, and backlog, partnering with data engineering teams to define ETL/ELT pipelines and integration requirements. Key responsibilities include designing data models for scientific entities like compounds and assays, ensuring FAIR principles, and collaborating with data scientists to provide analytics-ready datasets for AI/ML initiatives. Required skills include a strong scientific background, SQL, Python/PySpark, and Databricks experience. You will bridge the gap between science and data architecture to make complex research data discoverable and actionable.

What you'll do

  • Define and drive the holistic data strategy for modeling and managing connected scientific data in the enterprise data lake.
  • Partner with data engineering to shape ingestion, transformation, and integration pipelines for lab informatics systems.
  • Own the product vision, roadmap, and backlog for scientific data products based on scientific value and demand.
  • Design and govern data models that capture relationships across scientific entities like compounds, samples, and assays.
  • Translate stakeholder needs into clear data product requirements, acceptance criteria, and delivery plans.
  • Ensure data products meet integration, quality, and access requirements for Digital & Technology and analytics teams.
  • Provide data scientists with curated, analytics-ready datasets for modeling and AI/ML use cases.
  • Establish and uphold data quality, lineage, metadata, and FAIR principles across all scientific data products.

What we're looking for

  • Advanced degree in a biology discipline required.
  • PhD in molecular biology, biochemistry, genetics, or immunology (preferred).
  • BS/BA degree and/or MS degree in a related field required.
  • 10+ years of progressive experience managing scientific laboratory data, ideally within the biopharmaceutical or life sciences industry.
  • Strong scientific background in chemistry, biology, molecular biology, biochemistry, immunology, pharmacology, or a related discipline.
  • Hands-on experience with data modelling and connecting data across multiple source systems.
  • Working knowledge of data engineering concepts including data pipelines, ETL/ELT, and data transformation.
  • Strong SQL skills plus proficiency in a primary Databricks language such as SQL or Python/PySpark.
  • Product ownership or product management experience including roadmap definition, backlog prioritization, and stakeholder management.
  • Familiarity with transactional lab informatics systems such as ELN, LIMS, and instrument or analytical data platforms.
  • Hands-on experience with Databricks (or a comparable lakehouse platform) (preferred).
  • Experience leveraging AI to search, discover, and interrogate structured data (preferred).
  • Experience with knowledge graphs, ontologies, controlled vocabularies, or semantic data models (preferred).
  • Understanding of data lake/lakehouse architectures and modern data engineering practices (preferred).
  • Experience working with data scientists and analytics teams to deliver analytics-ready datasets (preferred).

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