Director, IT Scientific Solutions Architect
Incyte
1 month ago
Remote friendly (Wilmington, DE)
United States
IT
Responsibilities:
- Act as the primary point of contact for Discovery and Pre-Clinical teams on how scientific data should be captured, annotated, and organized to support decision making, reuse, and advanced analytics.
- Partner with scientific leaders to translate evolving research strategies into practical data enablement roadmaps (e.g., better experiment capture, cross-study comparability, data products that support key questions).
- Provide hands-on guidance to structure assays, studies, and results data for reuse across programs and modalities.
- Work with Advanced Analytics to identify high-value use cases dependent on Discovery/Pre-Clinical data (e.g., AI-driven target discovery, predictive modeling, multimodal integration) and ensure data is enablement-ready.
- Apply governance policies and standards within Discovery/Pre-Clinical data workflows in partnership with Data Governance & Compliance (without owning governance frameworks).
- Serve as a connector between scientific teams, Advanced Analytics, and Data Governance to ensure governance requirements are practical and high-value use cases are supported by appropriately governed data.
- Lead identification and definition of scientific data products (e.g., reusable assay result sets, cross-study biomarker packages, curated screening datasets).
- Ensure data products are well documented, discoverable, and FAIR aligned (Findable, Accessible, Interoperable, Reusable).
- Champion the shift from one-off data prep to repeatable, productized data assets.
- Contribute to data harmonization by defining meaningful mappings, terminologies, and ontologies; validate harmonization approaches remain fit for purpose.
- Support harmonization programs to enable scalable analytics and AI while focusing on data enablement.
- Act as a flexible lieutenant on cross-functional scientific data initiatives (e.g., digital lab capabilities, pilot AI projects, data-heavy partnerships).
- Take on special projects in ambiguous/emerging areas requiring scientific and data/digital insight.
- Champion scientific data literacy and reuse; demystify data products, FAIR, metadata, and AI readiness.
- Develop targeted communications/training/work sessions to improve data practices and reduce rework.
- Support change management for new data capabilities/workflows with Research IT, Data Governance, and Advanced Analytics.
Qualifications:
Required:
- Master’s or PhD in a Discovery or Pre-Clinical scientific discipline (e.g., biology, pharmacology, toxicology, immunology) or related field, with strong exposure to experimental research.
- 7+ years of experience working with Discovery and/or Pre-Clinical scientific data in pharma, biotech, or complex R&D environments.
- Hands-on experience with lab/scientific data systems (e.g., ELN, LIMS, SDMS; assay or instrument data integration) and knowledge of day-to-day scientific use.
- Demonstrated ability to work effectively with both bench scientists and technical teams (data engineering, analytics, IT).
Preferred:
- Experience contributing to or consuming harmonized scientific data models or ontologies.
- Familiarity with AI/ML use cases in R&D and data requirements (lineage, documentation, quality, representativeness).
- Prior involvement in multi-functional data programs (e.g., data quality improvements, data product development, analytics, digital lab initiatives).
- Strong communication and facilitation skills; ability to influence without owning governance or architecture.
Application instructions:
- Contact privacy@incyte.com for questions or concerns about exercising privacy rights.
- Act as the primary point of contact for Discovery and Pre-Clinical teams on how scientific data should be captured, annotated, and organized to support decision making, reuse, and advanced analytics.
- Partner with scientific leaders to translate evolving research strategies into practical data enablement roadmaps (e.g., better experiment capture, cross-study comparability, data products that support key questions).
- Provide hands-on guidance to structure assays, studies, and results data for reuse across programs and modalities.
- Work with Advanced Analytics to identify high-value use cases dependent on Discovery/Pre-Clinical data (e.g., AI-driven target discovery, predictive modeling, multimodal integration) and ensure data is enablement-ready.
- Apply governance policies and standards within Discovery/Pre-Clinical data workflows in partnership with Data Governance & Compliance (without owning governance frameworks).
- Serve as a connector between scientific teams, Advanced Analytics, and Data Governance to ensure governance requirements are practical and high-value use cases are supported by appropriately governed data.
- Lead identification and definition of scientific data products (e.g., reusable assay result sets, cross-study biomarker packages, curated screening datasets).
- Ensure data products are well documented, discoverable, and FAIR aligned (Findable, Accessible, Interoperable, Reusable).
- Champion the shift from one-off data prep to repeatable, productized data assets.
- Contribute to data harmonization by defining meaningful mappings, terminologies, and ontologies; validate harmonization approaches remain fit for purpose.
- Support harmonization programs to enable scalable analytics and AI while focusing on data enablement.
- Act as a flexible lieutenant on cross-functional scientific data initiatives (e.g., digital lab capabilities, pilot AI projects, data-heavy partnerships).
- Take on special projects in ambiguous/emerging areas requiring scientific and data/digital insight.
- Champion scientific data literacy and reuse; demystify data products, FAIR, metadata, and AI readiness.
- Develop targeted communications/training/work sessions to improve data practices and reduce rework.
- Support change management for new data capabilities/workflows with Research IT, Data Governance, and Advanced Analytics.
Qualifications:
Required:
- Master’s or PhD in a Discovery or Pre-Clinical scientific discipline (e.g., biology, pharmacology, toxicology, immunology) or related field, with strong exposure to experimental research.
- 7+ years of experience working with Discovery and/or Pre-Clinical scientific data in pharma, biotech, or complex R&D environments.
- Hands-on experience with lab/scientific data systems (e.g., ELN, LIMS, SDMS; assay or instrument data integration) and knowledge of day-to-day scientific use.
- Demonstrated ability to work effectively with both bench scientists and technical teams (data engineering, analytics, IT).
Preferred:
- Experience contributing to or consuming harmonized scientific data models or ontologies.
- Familiarity with AI/ML use cases in R&D and data requirements (lineage, documentation, quality, representativeness).
- Prior involvement in multi-functional data programs (e.g., data quality improvements, data product development, analytics, digital lab initiatives).
- Strong communication and facilitation skills; ability to influence without owning governance or architecture.
Application instructions:
- Contact privacy@incyte.com for questions or concerns about exercising privacy rights.