Felix Francis

About Felix Francis

I work in the space of R&D data, analytics, and scientific informatics with 14+ years of experience enabling enterprise-scale decision-making across pharmaceutical, biotech, and academic environments. My work focuses on building analysis-ready and AI-ready data products, enterprise data models, and governance frameworks that allow scientific and business teams to move faster — with confidence in the data behind their decisions. At Takeda, I lead data modeling, harmonization, and AI-ready data curation efforts that underpin advanced analytics, knowledge graphs, and AI initiatives across R&D. I partner closely with scientific, digital, and business leaders to translate complex R&D questions into scalable data assets, platforms, and operating models that deliver measurable impact. I have a strong track record of Designing enterprise data strategies aligned to R&D prioritiesLeading and mentoring high-performing technical teamsEstablishing FAIR data standards, governance, and reusable pipelinesBridging deep scientific expertise with modern data and AI ecosystemsEarlier in my career, I built and led bioinformatics and data science teams, developed widely used NGS and genomics tools, and published peer-reviewed research in top journals. This foundation allows me to connect hands-on scientific depth with enterprise-level strategy and execution. I am particularly passionate about responsible AI adoption in R&D, ensuring that data foundations, governance, and feature engineering are in place so AI can be trusted, scalable, and impactful. I enjoy working at the intersection of science, data, and leadership — and partnering with organizations looking to turn scientific complexity into reliable, decision-ready insights.

Experience

1. Associate Director, Data Modeling Lead

   Takeda

   Apr 2025 — Present

   Own the enterprise data modeling and harmonization strategy for R&D, enabling analysis-ready and AI-ready data products across discovery and development.• Lead cross-functional teams spanning data architecture, harmonization, governance, and analytics enablement to deliver scalable, reusable data assets.• Establish standards for FAIR data, lineage, quality, and interoperability, supporting advanced analytics, knowledge graphs, and AI initiatives.• Partner with R&D, Digital, and Business leadership to translate strategic scientific questions into prioritized data and analytics capabilities.• Drive platform evolution from site-based solutions to globally aligned, enterprise-grade data products, including dependency analysis and migration planning.• Enable responsible AI adoption by ensuring data readiness, feature engineering standards, and governance frameworks are embedded upstream.• Mentor and develop technical leaders, shifting execution from individual contribution to team-owned delivery.

Education

• Louisiana State University

  Masters

• University of Delaware

  Doctor of Philosophy (Ph.D.), Bioinformatics and Systems Biology

Skills

• Systems Biology
• Molecular & Cellular Biology
• Statistics
• Molecular Genetics
• Genetics
• Machine Learning
• Microbiology
• Microarray Analysis
• Algorithms
• Git
• Graph Algorithms
• Sas Programming
• Pandas
• Rnaseq
• Mysql
• Perl
• Bioinformatic Databases
• Django
• Qpcr
• Java
• Weka
• Linux
• Data Mining
• Bioinformatics
• Transcriptomics
• Network Analysis
• Computational Biology
• Genomics
• High Performance Computing
• Docker
• Unix
• R
• Python
• Latex