Skills map

Skills

My skills are strongest where scientific evidence has to be made useful for a decision: commercial strategy, adoption logic, governance, data interpretation and clear writing.

Evidence-backed

Core skill areas

Each skill below is tied to work I have actually done, the outputs it supported and why it matters in healthcare innovation.

Skill

Healthcare commercial strategy

Where I used it: Health Innovation East placement, QOF/adoption work, PulseOn strategy material and Charco/CUE1 business-case support.
What it supported: Evidence summaries, adoption logic, pathway framing, stakeholder decision-driver summaries and value reasoning.
Why it matters: Good strategy work has to connect clinical need, evidence quality, market context and the realities of adoption.

Skill

Evidence synthesis

Where I used it: HIE evidence briefs, market-analysis material, final-year dissertation framing and toxicology data mapping.
What it supported: Structured evidence tables, limitations-first summaries, source-to-claim mapping and clear written recommendations.
Why it matters: Evidence only helps when the strength, limits and relevance of each claim are visible.

Skill

AI/MedTech governance

Where I used it: AI Toolkit, final-year project governance handling, exploratory methods boundaries and AI-ECG adoption logic.
What it supported: Practical guidance-support material, governance framing, claim limits and adoption questions.
Why it matters: AI health tools need more than technical performance. They need evidence, safety, data governance, interoperability and monitoring.

Skill

Market intelligence and competitor analysis

Where I used it: CB Insights-supported market intelligence, secondary research and competitor-analysis materials across health technologies.
What it supported: Structured company lists, trend summaries, competitor matrices and adoption-readiness framing.
Why it matters: Commercial decisions need a clear view of who else is active, what evidence they have and how mature the space is.

Skill

Biomedical data analysis

Where I used it: Final-year project workflows, dose-toxicity extraction and cleaning, quantitative pharmacology and genetics/statistical reasoning.
What it supported: Feature-reasoning summaries, model-evaluation interpretation, cleaned tabular outputs and documented limitations.
Why it matters: Biomedical data work is strongest when the question, source limits and interpretation rules are clear before analysis begins.

Skill

Writing and communication

Where I used it: Guidance-support material, public case studies, dissertation writing, module assessments and presentation-style internship outputs.
What it supported: Concise summaries, advisory material, case-study narratives, limitations sections and decision-focused tables.
Why it matters: The work only lands if a reader can see the point, trust the limits and understand what should happen next.