Advisor - Data Strategy and Orchestration - Lilly Medicine Foundry
Scorpion Therapeutics, Indianapolis, Indiana, us, 46262
Role Summary
The Data Strategy & Data Orchestration Advisor is a principal-level technical expert responsible for researching, designing, and implementing innovative data architectures and methodologies to position the Medicine Foundry as a leader in pharmaceutical data management. This role develops novel approaches to data infrastructure, advances state-of-the-art pharmaceutical data technologies, and applies deep technical expertise to solve complex data challenges across the digital plant ecosystem. It involves pioneering data fabric architectures, real-time streaming platforms, ML-ready data infrastructures, and governance frameworks that balance regulatory compliance with operational agility. The advisor collaborates with Tech@Lilly architects and external partners to enable automated, paperless, and fully integrated operations, with roles fluid across project delivery and startup phases (2025–2028). Responsibilities
Research, design, and implement novel data architecture approaches for pharmaceutical manufacturing that advance industry capabilities, including data mesh architectures, data fabric platforms, real-time streaming solutions, and cloud-native data services. Develop innovative technical solutions for ML-ready data infrastructures including feature stores, MLOps platforms, model registries, experiment tracking systems, and automated ML pipeline orchestration that enable advanced analytics and AI applications. Design and implement advanced data platforms employing modern technologies including lakehouses (Databricks, Snowflake), distributed processing (Spark, Dask), streaming architectures (Kafka, Pulsar), and cloud-native services for scalable, high-performance data infrastructure. Collaborate with Tech@Lilly principal architects and engineers as a technical peer expert to design enterprise data architecture, evaluate emerging data technologies, and make platform decisions that balance innovation with operational requirements. Partner with the Augmented Intelligence Leader to build technical infrastructure enabling AI/ML innovation through proper data preparation, feature engineering capabilities, model governance frameworks, and responsible AI implementation. Develop innovative data governance frameworks and technical solutions that achieve cGMP compliance (21 CFR Part 11, EU Annex 11, ALCOA+) while enabling agility, including automated data quality monitoring, intelligent validation, and real-time audit trails. Drive research collaborations with academic institutions, industry consortia, and technology partners to explore emerging data management methodologies and establish thought leadership that positions Medicine Foundry as a leader in pharmaceutical data innovation. Design novel approaches to data democratization and self-service analytics in regulated environments, including semantic layers, knowledge graphs, automated metadata management, and AI-powered data discovery that enable business users while maintaining compliance. Establish enterprise data standards, metadata frameworks, and master data management approaches that ensure seamless data flows across MES, LIMS, SAP, Sample Management, and other digital plant systems supporting the unified digital thread vision. Lead multi-functional data governance council with PR&D, M&Q, QC, QA, and Engineering representatives to align on data requirements, quality metrics, integration approaches, and technical solutions for data integrity and regulatory compliance. Build and mentor a high-performing team of data professionals, coaching them in advanced data technologies, innovative problem-solving approaches, and technical excellence while fostering their growth in specialized areas. Qualifications
Required:
Bachelor's, Masters or PhD in Data Science, Computer Science, Data Engineering, or related technical field. Required:
12+ years (BS), 8+ years (MS), or 2+ years (PhD) in data architecture, data engineering, or related technical fields with a proven track record of researching, developing, and implementing novel data solutions in complex environments. Required:
Deep hands-on technical expertise in cloud platforms (AWS, Azure, GCP), distributed systems (Spark, Dask), streaming architectures (Kafka), and advanced data integration patterns with proven ability to design and implement enterprise-scale solutions. Required:
Research-level expertise in data architecture methodologies including data mesh, data fabric, lakehouse architectures, real-time analytics, and ML infrastructure with demonstrated history of advancing beyond industry standard practices. Required:
Strong knowledge of pharmaceutical M&Q systems (MES, LIMS, ERP, Sample Management) and experience designing data architectures that integrate complex manufacturing and quality systems while maintaining regulatory compliance. Additional Preferences
Experience in pharmaceutical, biotechnology, or other FDA-regulated industries with deep understanding of cGMP data integrity requirements and regulated manufacturing data environments. Track record of technical innovation demonstrated through publications, patents, conference presentations, open-source contributions, or recognized achievements in advancing data management methodologies in pharmaceutical or life sciences domains. Hands-on experience implementing pioneering data solutions such as data mesh architectures, real-time streaming analytics, knowledge graphs, feature stores, MLOps infrastructure, or data fabric platforms in production enterprise environments. Experience with semantic data layers, graph databases, vector databases, data observability platforms, and AI-powered data management tools with ability to evaluate and implement novel approaches. Experience working in highly matrixed pharmaceutical organizations and partnering effectively with R&D technical leaders and IT architects to deliver innovative yet pragmatic data solutions. Entrepreneurial mindset with demonstrated ability to pioneer innovative approaches, challenge existing practices, and drive technical innovation in ambiguous, fast-paced environments requiring creativity, resilience, and high learning agility. Education
Bachelor’s, Master’s, or PhD in Data Science, Computer Science, Data Engineering, or related technical field.
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The Data Strategy & Data Orchestration Advisor is a principal-level technical expert responsible for researching, designing, and implementing innovative data architectures and methodologies to position the Medicine Foundry as a leader in pharmaceutical data management. This role develops novel approaches to data infrastructure, advances state-of-the-art pharmaceutical data technologies, and applies deep technical expertise to solve complex data challenges across the digital plant ecosystem. It involves pioneering data fabric architectures, real-time streaming platforms, ML-ready data infrastructures, and governance frameworks that balance regulatory compliance with operational agility. The advisor collaborates with Tech@Lilly architects and external partners to enable automated, paperless, and fully integrated operations, with roles fluid across project delivery and startup phases (2025–2028). Responsibilities
Research, design, and implement novel data architecture approaches for pharmaceutical manufacturing that advance industry capabilities, including data mesh architectures, data fabric platforms, real-time streaming solutions, and cloud-native data services. Develop innovative technical solutions for ML-ready data infrastructures including feature stores, MLOps platforms, model registries, experiment tracking systems, and automated ML pipeline orchestration that enable advanced analytics and AI applications. Design and implement advanced data platforms employing modern technologies including lakehouses (Databricks, Snowflake), distributed processing (Spark, Dask), streaming architectures (Kafka, Pulsar), and cloud-native services for scalable, high-performance data infrastructure. Collaborate with Tech@Lilly principal architects and engineers as a technical peer expert to design enterprise data architecture, evaluate emerging data technologies, and make platform decisions that balance innovation with operational requirements. Partner with the Augmented Intelligence Leader to build technical infrastructure enabling AI/ML innovation through proper data preparation, feature engineering capabilities, model governance frameworks, and responsible AI implementation. Develop innovative data governance frameworks and technical solutions that achieve cGMP compliance (21 CFR Part 11, EU Annex 11, ALCOA+) while enabling agility, including automated data quality monitoring, intelligent validation, and real-time audit trails. Drive research collaborations with academic institutions, industry consortia, and technology partners to explore emerging data management methodologies and establish thought leadership that positions Medicine Foundry as a leader in pharmaceutical data innovation. Design novel approaches to data democratization and self-service analytics in regulated environments, including semantic layers, knowledge graphs, automated metadata management, and AI-powered data discovery that enable business users while maintaining compliance. Establish enterprise data standards, metadata frameworks, and master data management approaches that ensure seamless data flows across MES, LIMS, SAP, Sample Management, and other digital plant systems supporting the unified digital thread vision. Lead multi-functional data governance council with PR&D, M&Q, QC, QA, and Engineering representatives to align on data requirements, quality metrics, integration approaches, and technical solutions for data integrity and regulatory compliance. Build and mentor a high-performing team of data professionals, coaching them in advanced data technologies, innovative problem-solving approaches, and technical excellence while fostering their growth in specialized areas. Qualifications
Required:
Bachelor's, Masters or PhD in Data Science, Computer Science, Data Engineering, or related technical field. Required:
12+ years (BS), 8+ years (MS), or 2+ years (PhD) in data architecture, data engineering, or related technical fields with a proven track record of researching, developing, and implementing novel data solutions in complex environments. Required:
Deep hands-on technical expertise in cloud platforms (AWS, Azure, GCP), distributed systems (Spark, Dask), streaming architectures (Kafka), and advanced data integration patterns with proven ability to design and implement enterprise-scale solutions. Required:
Research-level expertise in data architecture methodologies including data mesh, data fabric, lakehouse architectures, real-time analytics, and ML infrastructure with demonstrated history of advancing beyond industry standard practices. Required:
Strong knowledge of pharmaceutical M&Q systems (MES, LIMS, ERP, Sample Management) and experience designing data architectures that integrate complex manufacturing and quality systems while maintaining regulatory compliance. Additional Preferences
Experience in pharmaceutical, biotechnology, or other FDA-regulated industries with deep understanding of cGMP data integrity requirements and regulated manufacturing data environments. Track record of technical innovation demonstrated through publications, patents, conference presentations, open-source contributions, or recognized achievements in advancing data management methodologies in pharmaceutical or life sciences domains. Hands-on experience implementing pioneering data solutions such as data mesh architectures, real-time streaming analytics, knowledge graphs, feature stores, MLOps infrastructure, or data fabric platforms in production enterprise environments. Experience with semantic data layers, graph databases, vector databases, data observability platforms, and AI-powered data management tools with ability to evaluate and implement novel approaches. Experience working in highly matrixed pharmaceutical organizations and partnering effectively with R&D technical leaders and IT architects to deliver innovative yet pragmatic data solutions. Entrepreneurial mindset with demonstrated ability to pioneer innovative approaches, challenge existing practices, and drive technical innovation in ambiguous, fast-paced environments requiring creativity, resilience, and high learning agility. Education
Bachelor’s, Master’s, or PhD in Data Science, Computer Science, Data Engineering, or related technical field.
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