Data · Decisions · Product

Former strategy consultant turned data scientist.

I start with the problem and use context, data and code to build solutions that solve real needs.

Machine Learning LLM / GenAI Agentic Systems Design Thinking Decision Science
Neil Sinclair

01 / Who I Am

Strategy, data & product — where they overlap.

With an MBA and a Masters in Data Science, I've had seven years of consulting and innovation experience — two in strategy, five in data science. I've worked predominantly for some of the largest firms in Africa and Europe, helping them identify and solve their biggest data and decision-related challenges.

Too often, data science produces elegant models that don't speak to real needs. My time as a strategy consultant taught me that context and customers matter more than theory: start with the problem first, test and iterate with the user second, deliver the solution third.

  • 01Design-thinking workshop to surface the real challenges.
  • 02Prioritise problems by impact and ease.
  • 03Build lean: prototype, test with users, adjust.
  • 04Measure results, refine, and scale what works.

02 / Selected Work

My Experience

Agentic Workflow Automation • LLM • Finance

LLM-driven Research Workflow

Built an LLM-driven finance research workflow. Periodically extracts information from Frontier AI labs' papers, sites & X-posts and scores the announcements' impact on a hypothetical share portfolio.

Online Tool →

Multi-Agent • LLM • iPhone App

Multi-Agent Kitchen Assistant

Built the multi-agent kitchen assistant for the Food Storii start-up in summer 2026. Assistant managed the user's digital twin kitchen creating recipes, meal plans and managing inventory.

Project closed

Optimisation • Business Case Validation

Heat Pump & TES Optimisation

Built a custom heat pump & thermal energy storage optimisation tool for an early stage entrepreneur. Through numerous stages the tool helped guide the development of the initial business case.

Online Tool →

04 / Contact

Got an exciting data challenge?

Reach out and let's talk about how context, data and code can solve it.

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