Lab Informatics Lead

Role Summary

Lead the design, implementation, and optimization of Takeda’s next-generation laboratory informatics ecosystem, spanning data pipelines, robotics data capture and integration, and AI-assisted decision systems, with integrations to ELN/LIMS. Ensure end-to-end digitized workflows with complete data traceability, automation readiness, and AI-enriched insights. Architect a modular, API-first informatics platform enabling seamless interoperability across tools and platforms to enable a fully automated lab.

Responsibilities

• Owning the end-to-end strategy, roadmap, and architecture for automated digital lab systems across Research, aligned with our Global DD&T Architecture teams.
• Develop and operationalize advanced assay analytics including AI-assisted, automated high throughput statistical and curve-fitting pipelines (IC50/EC50, dose-response modeling, non-linear regression, data and instrument QC, outliner detection, drift analysis).
• Develop harmonized experiment templates, assay workflows, and metadata models to ensure scientific and operational consistency across teams and global sites.
• Work within governance frameworks ensuring data integrity, traceability, audit readiness, and full lifecycle management of scientific records.
• Architect a modular, API-first informatics ecosystem enabling seamless interoperability between ELN/LIMS, automation platforms, analytics tools, and orchestration platforms to enable a fully automated lab.
• Design ELN/LIMS schemas and workflow logic that support automated execution, robot-ready instructions, worklist generation and closed-loop optimization.
• Drive development of visualization and dashboarding tools for real-time experiment monitoring, QC insights, throughput metrics, and scientific interpretation.
• Ensure all data is FAIR, structured, analytics-ready, and integrated with downstream modeling, ML workflows, and portfolio-level insights.
• Serve as the primary owner of relationships with digital lab technology partners, and automation/digital ecosystem suppliers that support our automated labs.

Qualifications

• 8+ years of experience in R&D informatics, digital lab systems, high-throughput data workflows, or scientific software.
• Leadership experience owning lab informatics platforms and designing integrated scientific informatics architectures.
• Experience supporting automated labs, robotics-enabled workflows, and high-throughput/ultra-high-throughput experimentation.
• Strong background in scientific data analysis data QC, curve fitting, statistical modeling, dose–response analytics, and visualization.
• Experience delivering scalable data pipelines, dashboards, and automated reporting.

Skills

• Strong understanding of scientific data structures, assay workflows, metadata models, and traceability frameworks.
• Expertise integrating instruments, automation systems, and robotics with ELN/LIMS and analytics tools, specifically Benchling and Revvity Signals.
• Proficiency in high-throughput data analytics (IC50/EC50 modeling, kinetics, HTS scoring, and anomaly detection).
• Familiarity with cloud-native data engineering, workflow orchestration, and scalable compute environments.
• Working knowledge of AI/ML applications supporting lab workflows (predictive QC, automated annotation, LLM augmentation).
• Demonstrated success designing, deploying, operating, and supporting laboratory automation platforms and scheduling software, such as HighRes Biosolutions Cellario, BioSero Green Button Go, Thermo Fisher Momentum, or equivalent systems.
• Hands-on experience operating and programming automated liquid handlers, plate readers, imagers, incubators, and associated equipment, with an understanding of assay- and hardware-driven constraints.
• Proficiency in one or more modern programming languages (e.g., Python, C#, Java, C++), applied to automation control, workflow orchestration, data processing, or system integration.
• Technical expertise in relational database technologies, data modeling, and SQL.

Education

• Bachelor’s, Master’s, or PhD in Computer Science, Bioinformatics, Data Science, Engineering, or related fields.