R&D Data Scientist

As a Data Scientist - R&D Systems & Data Governance at Elanco, you will serve as a strategic technical leader bridging advanced data science and data governance. In this role, you will define, establish, and operationalize R&D wide data governance, policies, and standard operating procedures (SOPs) from a systems perspective. You will provide critical advisory on which data attributes, metadata standards, ontologies, and master data models must be controlled, standardized, and harmonized at R&D specific levels.

Reporting into the R&D organization and closely partnering with Enterprise Data Office (EDO), Operations, and IT, you will drive foundational data readiness across key R&D scientific platforms-including ARCHI, Benchling (ELN / Registry / Inventory), Revvity (Signals / Informatics), and Clinical Systems (EDC, CTMS, eCOA/ePRO). You will ensure that research, discovery, and clinical trial data are structured, lineage-tracked, and AI-ready to accelerate pipeline breakthroughs and regulatory submissions.

This includes four strategic priorities: * R&D Governance & Systems Policy: Support the interoperability of Data across R&D and Enterprise-wide ecosystems. * Attribute-Level Data Architecture: Determine and advise on critical R&D data attributes, controlled vocabularies, schemas, and master data entities required to ensure cross-system interoperability. * Pipeline Acceleration & AI Readiness: Optimize data capture, extraction, and standardization across preclinical and clinical workflows to power machine learning, generative AI, and predictive modeling. * Data Quality & Compliance Excellence: Establish automated data quality frameworks, lineage monitoring, and compliance controls (FAIR principles, GxP, ALCOA+) across interconnected scientific systems.

Your Responsibilities: * R&D Data Governance & Policy Formulation: Author, establish, and govern enterprise-wide data policies, standards, data classification, and operating procedures across R&D software platforms, ensuring seamless alignment between scientific workflows and enterprise IT architecture. * Attribute-Level Control & Metadata Management: Evaluate data models across scientific applications to define what must be controlled at the enterprise level (e.g., compound IDs, study attributes, subject identifiers, assay parameters) versus local system configurations; establish standardized ontologies and controlled vocabularies. * R&D Systems Integration & Data Strategy: Serve as the lead data science advisor for core discovery and development platforms: + ARCHI: Standardize enterprise architecture definitions, repository schemas, and data pipelines. + Benchling: Oversee data schema design, entity registration, inventory tracking, and API-based data extraction for downstream analytics. + Revvity : Guide assay data capture, screening data harmonization, and analytical workflow standardization. + Clinical Systems: Ensure clinical trial data attributes (EDC, CTMS, CDISC/SDTM models) adhere to enterprise standards for traceability and submission readiness. * End-to-End Data Science & Modeling: Design, build, and deploy machine learning and statistical models that leverage harmonized data assets from Benchling, Revvity, and Clinical platforms to solve complex R&D challenges (e.g., predictive screening, clinical outcome modeling). * Data Quality Frameworks & Automated Monitoring: Implement automated data validation, anomaly detection, and data completeness metrics at ingestion points across enterprise endpoints. * Cross-Functional Influence & Stakeholder Management: Partner with senior stakeholders across Discovery, Translational Medicine, Clinical Operations, Regulatory, Quality Assurance, and IT to drive adoption of data governance practices without impeding scientific velocity. * Collaborate on Infrastructure & Deployment: Work alongside Data Engineers, ML Engineers, and Cloud Solution Architects (Azure / GCP) to design scalable data pipelines, data mesh/lakehouse architectures, and secure data access layers. * Champion FAIR Data & Life Science Compliance: Embed FAIR (Findable, Accessible, Interoperable, Reusable) data principles and ensure data governance workflows meet regulatory standards (GxP, 21 CFR Part 11, ALCOA+ data integrity guidelines).

Education: Bachelors degree or Masters in Data Science, Computer Science, Bioinformatics, Computational Biology, Information Systems, Operations Research, or a related quantitative/life sciences discipline. * Required Experience: 8+ years of relevant experience in Data Science, R&D Informatics, or Data Architecture within Life Sciences, Biotechnology, Animal Health, or Pharmaceuticals. * Data Governance & Systems Expertise: Proven experience designing data governance policies, attribute-level master data management (MDM), data catalogs, data dictionaries, and enterprise data models from a systems perspective. * R&D & Clinical Systems Knowledge: Hands-on experience working with or integrating modern life science scientific platforms, specifically:

• Electronic Lab Notebooks & Registries (e.g., Benchling).
• Scientific Informatics & Chemistry/Biology platforms (e.g., Revvity Signals, PerkinElmer informatics).
• Architecture & Enterprise modeling tools (e.g., ARCHI / ArchiMate).
• Clinical data platforms (EDC, CTMS, CDISC standards). * Programming & Querying Proficiency: Strong programming skills in Python or R, along with advanced SQL capabilities for relational, document, and graph database querying. * Data Science & ML Capabilities: Demonstrated experience developing and applying statistical and machine learning models (e.g., classification, regression, clustering, NLP for scientific text, predictive modeling). * Communication & Data Storytelling: Exceptional ability to synthesize complex data architecture and analytical concepts into actionable recommendations for scientific, operational, and executive stakeholders.

What Will Give You the Competitive Edge (Preferred Qualifications): * Life Science Ontologies & Metadata Standards: Deep familiarity with biomedical ontologies (e.g., MeSH, SNOMED, ChEMBL, CDISC/SDTM/ADaM) and FAIR data implementation frameworks. * API & Pipeline Engineering: Experience with REST APIs, ETL/ELT pipelines, and integration of scientific SaaS platforms into enterprise data lakes/lakehouses (e.g., Snowflake, Databricks). * Cloud Analytics Platforms: Familiarity with data and analytics services across Microsoft Azure (e.g., Azure Synapse, Purview, Data Factory) and/or Google Cloud Platform (GCP). * Regulatory & Quality Knowledge: Working knowledge of GxP, 21 CFR Part 11, data privacy requirements, and computerized system validation (CSV) in life science environments.

We offer a comprehensive benefits package focusing on financial, physical, and mental well-being while encouraging our employees to pursue our purpose! Some highlights include:

• Multiple relocation packages
• Two weeklong shutdowns (mid-summer and year-end) in the US (in addition to PTO)
• 8-week parental leave
• 9 Employee Resource Groups
• Annual bonus offering
• Flexible work arrangements
• Up to 6% 401K matching

At Elanco (NYSE: ELAN) - it all starts with animals!

As a global leader in animal health, we are dedicated to innovation and delivering products and services to prevent and treat disease in farm animals and pets. At Elanco, we are driven by our vision of Food and Companionship Enriching Life and our purpose - all to Go Beyond for Animals, Customers, Society and Our People.

At Elanco, we pride ourselves on fostering a diverse and inclusive work environment. We believe that diversity is the driving force behind innovation, creativity, and overall business success. Here, you’ll be part of a company that values and champions new ways of thinking, work with dynamic individuals, and acquire new skills and experiences that will propel your career to new heights.

Making animals’ lives better makes life better - join our team today!