Selected Work

1Two production AI agents, gated on evaluation, inside a regulated insurer

Director of Climate Risk Products and Applied AI · CarbonPool

Days → under 10 minutes
~80% of team on AI-augmented workflows
2 agents in production

Problem

Technical project assessment and counterparty due diligence took several days of manual analyst work, and in an insurance context the output carries liability, so speed could not come at the cost of correctness.

What I did

Architected, built, and deployed two production LLM agents with Claude Code: an applied research agent for technical project assessment, and a due-diligence / KYC agent for project developers, investors, and documentation. Each was gated on the model-evaluation pipeline before any client use. I also rolled out Claude Code to the modelling team and Cowork to the C-suite.

Outcome

Manual turnaround cut from several days to under 10 minutes. Around 80% of the team onboarded onto AI-augmented workflows, with time saved measured per workflow.

Skills

Production agent development
Evaluation-first governance
AI adoption across a regulated org
Tech-to-non-tech translation

2Zero to $XX M: a risk model behind a first-of-kind insurance product

Technical Team Founding Member (Employee #5) · CarbonPool

$XX M pre-underwritten premiums
50% faster pre-underwriting
100+ projects evaluated

Problem

A new carbon-credit insurance product needed a risk model that did not yet exist, built to standards an insurer and its regulator could stand behind.

What I did

Built the 0-to-1 risk and model-evaluation pipeline and architected the end-to-end risk-modelling stack and AWS structure to insurance-grade reproducibility. Established model governance, documentation, and data-quality protocols across 4 teams, and evaluated 100+ carbon-removal projects across nature-based and engineered pathways on Verra, Gold Standard, ACR, and CAR registries. The uncertainty methodology was reviewed by FINMA. Rebuilt the analytical codebase with Claude Code as advisor, writer, and reviewer.

Outcome

The model generated $XX M in pre-underwritten premiums in year one. Pre-underwriting time cut 50% and the assessable project portfolio expanded. Analytical turnaround cut ~97%, recovering around $1,000/day in analyst hours.

Skills

0-to-1 risk modelling
Regulatory accountability
Model governance
Technical architecture
Portfolio and pricing input

3TCFD physical-risk disclosure for enterprise clients at McKinsey

Research Science Specialist (Climate Data Scientist) · McKinsey & Company

~$XX M revenue-at-risk per client
3 enterprise clients
30+ AI/ML technologies assessed

Problem

Three top enterprise clients needed TCFD physical-risk disclosures that quantified financial exposure credibly enough for C-suite, risk, and technical stakeholders.

What I did

Delivered asset-level climate-hazard exposure analysis using Python, statistical modelling, and geospatial visualisation, and presented findings across stakeholder levels. Separately, built and led a competitive-intelligence programme covering 30+ AI/ML climate and decarbonisation technologies.

Outcome

Quantified approximately $XX M in revenue-at-risk per client. The competitive-intelligence investment thesis was adopted by the firm's green-business-building practice and published in a firm report.

Skills

Financial-impact quantification
Executive communication
Geospatial and statistical modelling
Competitive intelligence

1Two production AI agents, gated on evaluation, inside a regulated insurer

Problem

What I did

Outcome

Skills

2Zero to $XX M: a risk model behind a first-of-kind insurance product

Problem

What I did

Outcome

Skills

3TCFD physical-risk disclosure for enterprise clients at McKinsey

Problem

What I did

Outcome

Skills

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