Scientific proposition
What decision does the product improve, what evidence supports it and where are the method or applicability boundaries?
Thinking clearly about science-led decisions
Assessing scientific software beyond the demo
The scientific proposition, software architecture and delivery path need to hold together before a major commitment.
I am interested in how teams assess products built around chemistry, molecular data, predictive models and AI-enabled R&D—especially where generic software questions miss scientific risk.
Questions worth resolving early
Is the product credible beyond the demo?
A useful review connects what the product claims scientifically with what the system can support technically and operationally.
Data foundations
Are the important entities, properties, provenance and quality assumptions represented consistently enough to support the claim?
Model integration
Can predictive capability be accessed, validated, monitored and explained inside the workflows where it creates value?
Software architecture
Which parts are maintained platform capability, which are prototype shortcuts and where will repeated integration create risk?
Delivery path
Does the roadmap address validation, reliability, ownership and adoption—or only the next visible feature?
Team and adoption
Can scientists, developers and product decision-makers work from a shared understanding of the system and its limitations?
Assessment approach
Trace the claim through the system.
I tend to trace the chain from scientific intent to data, models, interfaces, workflow and user decision. This helps distinguish an ordinary early-stage gap from a structural risk that could require a different roadmap.
- Understand the claim: identify the scientific and product assertions the system must support.
- Inspect the evidence: review the available architecture, workflows, validation approach and operating assumptions.
- Find the joins: focus on hand-offs between science, data, models, software and users where risk is often hidden.
- Prioritise findings: separate immediate blockers, material risks, reasonable stage-dependent debt and longer-term opportunities.
The perspective I bring
Scientific depth, engineering substance and product judgement.
The combination matters because a generic software checklist rarely captures the full scientific context.
Chemistry and modelling
PhD-trained chemistry background with practical experience of computational methods, scientific validation and model-versus-evidence thinking.
Hands-on scientific software
Hands-on Python contribution across APIs, maintained clients, predictive workflows, validation, performance and scientist-facing tools.
Platform and adoption leadership
Experience translating detailed scientific requirements into platform scope, governance, delivery choices and shared organisational capability.
Clear boundaries
Technical assessment is only one part of diligence.
Scientific-software credibility, architecture, data and model dependencies, validation, maintainability and delivery risk all matter. They sit alongside—not in place of—legal, financial, regulatory, cybersecurity and formal scientific-validation expertise.
- Technical challenge grounded in scientific context
- Assessment depth matched to the stage and decision
- Clear separation between evidence, assumptions and open questions
- Reasoning expressed for technical and decision-making audiences
Questions about technical assessment
What good diligence needs to answer.
Is technical due diligence only for investors?
No. The same questions are useful when considering a platform direction, major build, external partnership or technical roadmap.
At what stage do these questions become useful?
They become especially useful when an early prototype is becoming a product, before a significant engineering commitment or when an existing capability needs a fresh architecture and delivery assessment.
What evidence supports a useful assessment?
Product claims, architecture diagrams, technical documentation, representative workflows, validation evidence and the perspectives of people building and using the system all contribute.
A shared technical interest
Interested in the same questions?
If your work intersects with scientific software assessment, data platforms or predictive workflows, you are welcome to get in touch.