Case studies · Lead Data Scientist

Work designed to change the next decision.

I help businesses turn messy commercial and operational data into better decisions.

I work on problems where better forecasting, experimentation or modelling can make the next action clearer.

Case studies

01

Pricing & forecasting

Marketplace Pricing & Demand Forecasting

Turning noisy demand and capacity signals into actionable pricing decisions.

Decision: When and how much to intervene — and when to leave price alone.

Demand controlled by a pricing intervention Observed demand rises toward an intervention point. Without intervention, forecast demand exceeds usable capacity. After a twelve percent pricing intervention, projected demand finishes at capacity. Usable capacity Without intervention After pricing intervention
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02

Experimentation

Experimentation & Causal Decision Making

Designing experiments that answer commercial decisions, not merely produce statistically significant results.

Decision: Whether a commercial change should be rolled out, and what risk that decision accepts.

Experiment effect clears the rollout threshold The entire confidence interval is positioned beyond the minimum worthwhile effect, with its point estimate in the commercial upside range. Do not roll out Minimum worthwhile effect Commercial upside
Guardrails within tolerance Roll out with monitoring
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03

Commercial analytics

Commercial Data Rescue

Turning fragmented operational signals into a decision tool that tells teams where to look first.

Decision: Find the likely cause of a performance change quickly and focus investigation.

Trading movement diagnostic Illustrative data
Detected Below expected range
Look first Supply / capacity
  • DemandStableWithin expected range
  • ConversionWatchSoft in one segment
  • PriceStableMix-adjusted movement limited
  • Supply / capacityPriorityPeak-period constraint
  • Data qualityClearCore checks passing
An illustrative trading diagnostic highlighting supply and capacity as the first investigation path.
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Case studies use anonymised descriptions and illustrative data. No confidential or proprietary employer/client data is shown.