Associate Director Solutions Partner (Scientific Data Product Owner)

About the role

We are seeking an Associate Director Solutions Partner to lead the holistic data strategy for modelling and managing connected scientific data in our enterprise data lake. This role involves defining and driving the data strategy, partnering with data engineering to define and shape data pipelines, owning the product vision and backlog, and collaborating with various stakeholders.

Responsibilities

• Define and drive the holistic data strategy for modelling and managing connected scientific data in the data lake.
• Ensure data flowing from transactional lab informatics systems is harmonized, contextualized, and connected.
• Partner with data engineering to define and shape ingestion, transformation, and integration pipelines (ETL/ELT).
• 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 capturing relationships across scientific entities to make data connected rather than isolated.
• Translate the needs of scientific, Digital & Technology, and analytics stakeholders into clear data product requirements, acceptance criteria, and delivery plans.
• Partner with Digital & Technology teams to ensure data products meet their integration, quality, and access requirements.
• Partner with data scientists to ensure 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 the use of AI-driven approaches for searching and discovering structured scientific data.
• Act as the primary point of contact and advocate for scientific data products, gathering feedback and continuously improving usability, coverage, and value.

Requirements

• Bachelor's or Master's degree in a related field required.
• 10+ years of progressive experience managing scientific laboratory data, preferably within the biopharmaceutical or life sciences industry.
• Strong scientific background, with the ability to understand the meaning and context of the data being modelled.
• Hands-on experience with data modelling and an understanding of how to connect data across multiple source systems.
• Ability to drive diverse stakeholders to alignment on desired outcomes, and to 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, proficiency in a primary Databricks language (e.g., SQL, Python/PySpark), and 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 the ability to bridge scientific, technical, and analytics audiences.
• Advanced degree in a biology discipline required; PhD in molecular biology, biochemistry, genetics, or immunology preferred.

Qualifications

• 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.

Skills

• Strong scientific background (e.g., chemistry, biology, molecular biology, biochemistry, immunology, pharmacology, or a related discipline).
• Hands-on experience with data modelling and an understanding of how to connect data across multiple source systems.
• Ability to drive diverse stakeholders to alignment on desired outcomes, and to 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, proficiency in a primary Databricks language (e.g., SQL, 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 the ability to bridge scientific, technical, and analytics audiences.
• Advanced degree in a biology discipline required; PhD in molecular biology, biochemistry, genetics, or immunology preferred.
• 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.

Pay

$216,100.00 - $360,200.00 annually

Schedule

4 days onsite per week required