Your decisions matter.
Make your scientific data decision-ready.
Iris Scientific bridges lab and computational scientists, project leaders, and informatics and IT teams, translating research needs into practical solutions, connected systems, and reusable data.
Partner with us to reduce data-cleaning bottlenecks, put your scientific data in context, connect it across teams and workflows, and make it action-ready for analysis and decisions.
Reduce data-cleaning bottlenecks, put your scientific data in context, and connect it across teams and workflows.
How Iris Scientific can help
Focused projects and connected support across strategy, systems, workflows, and decisions.
Landscape analysis
- Current state & priorities
- Requirements & roadmap
- Build vs. buy decisions
- Software & Platform evaluation and selection
Repositories & metadata
- Interoperability
- Integration
- Governance
- Data hygiene
Production workflows
- Data ingestion & QC
- Lab support workflows
- Automated reporting
- Human-in-the-loop
Scientific data curation & modeling-ready datasets
- Internal, CRO & public sources
- Quality & provenance checks
- Harmonization assessment
- Documented, model-ready datasets
Modeling & decision support
- Cheminformatics
- SAR analysis
- Modeling-ready data
- Custom solutions
Scientific applications
- Customer workflow and pain point discovery
- Product development strategy
- Scientific requirements and use-case definition
- Data model and workflow design
Better-informed decisions. Less manual data work.
Proposed an approach for a scientific software company to help drug discovery teams adopt an existing FAIR data concept and improve data interoperability. The approach was incorporated into the company's product roadmap.
Supporting a drug discovery AI/ML prediction platform by generating datasets and building a lightweight, tailored data analysis application. The results of the analyses will be used to demonstrate the platform's capabilities in marketing materials.
Led development, roll-out, and adoption across distributed medicinal chemistry teams at a major pharmaceutical company's global R&D sites, cutting analytical variability and enabling transparent, cross-team decisions.
Designed a production-grade pipeline for CRO data at a scaling biotech, eliminating manual data handling and cutting time-to-analysis.
Saved hours weekly in lab operations and eliminated risk of manual error.
Identified and implemented a workflow change enabling efficient quality checking — improving data confidence at no added cost.
Diagnose
Understand your needs. Identify opportunities.
Select + design
Evaluate, prototype, and pressure-test options.
Build + operationalize
Make it work in practice. Train users.
First-hand discovery experience. Systems thinking. Practical delivery.
With 20+ years in pharma and biotech discovery, including AstraZeneca and Jnana Therapeutics, my background spans computational chemistry, cheminformatics, and scientific informatics.
I bridge bench and computational scientists, project leaders, managers, informatics and IT teams, vendors, and partners — translating research needs into reusable data, connected systems, and practical workflows that support better decisions and greater value from research investments.