Our Services
Four disciplines, one practice.
Signal & Craft works with hedge funds, asset managers, banks, data providers and technology firms across four overlapping disciplines. Each engagement is scoped to the specific question on the table, and the disciplines themselves are designed to sit together.
Talent
A technical interview stage you can rely on.
The way capital markets teams hire technical staff has not kept pace with the way technical work has changed. Whiteboard interviews gave way to take-home tests. Take-home tests have now been quietly outsourced to Claude, ChatGPT and a long tail of specialised assistants. The methods that once told you whether a candidate could think have stopped doing so, and most hiring teams cannot reliably tell the difference.
Signal & Craft adds a stage to your hiring process for portfolio managers, analysts, data scientists, quants and engineers. Working alongside your existing team, I assess candidates in ways that reflect how the work is genuinely done in 2026: with judgement, with tools, and under the kind of pressure that surfaces real understanding rather than rehearsed answers.
What you get out of it
For COOs. Better hires the first time, and far fewer of the costly displacements that follow when a senior technical hire turns out to be the wrong fit. Severance, lost research momentum and the impact on team morale make the cost of an additional assessment stage look undeniably sensible.
For team leads. A technical voice that sits entirely outside your organisational politics. No internal sponsorship of one candidate over another, no preferred team to staff up, no agenda beyond getting the read right. Independence is often the most useful part of the engagement.
For HR and recruiting partners. A structural difference that matters: we are not paid on placement. The compensation model that biases external recruiters towards any close-enough candidate does not apply here. The incentive is to give you the most accurate assessment possible, including when the answer is no.
Why Signal & Craft
The work draws on almost two decades on the buy-side at some of the most sophisticated firms in their respective industries, together with ongoing academic research at UCL into AI in quantitative finance. That combination of operating experience and current technical depth is what allows the assessments to be genuinely useful rather than ceremonial, not to mention having conducted several hundred interviews across various roles and organisations, from interns to senior executives (including CXOs).
Tech
A fractional CTO whose home discipline is quant.
New quant and data science teams typically face the same set of technology decisions, often without anyone in-house who has made those decisions before. The cost of getting them wrong rarely shows up at the time and compounds exponentially through time. Getting it right early on makes a meaningful difference, otherwise, it shows up months later, in slow research cycles, painful migrations and tooling that does not match how the team actually works.
Signal & Craft works with hedge funds, asset managers, banks and technology firms on reviews and implementations across the research, data and trading stacks. Engagements are scoped from short reviews of an existing platform through to multi-month implementation oversight.
Where we work
Across the tech stack, from data infrastructure, research environments, version control, model development pipelines, vendor selection, cloud architecture, and the practical question of how a research team should organise itself around its tools. The right answer is rarely the most fashionable one, and almost never involves buying everything on the slide deck. When the priorities of risk and compliance clash with the budget of the CFO/COO, and the requirements of the technical team requesting them, it makes sense to have someone technical in the room early on, not least due to the proliferation of tools, noteably those littered with AI buzzwords.
Why Signal & Craft
Think of it as a fractional CTO whose home discipline is quant. I have built and led data engineering and data science functions inside an asset manager, traded on the buy-side at one of the worlds most successful hedge funds, and continue to do active quantitative research at UCL. The architectural advice is grounded in what the team will actually need once the platform is live, not in a generic enterprise reference architecture.
Typical outcomes include a clearer technology roadmap, a sharper view of build versus buy across the stack, faster onboarding for new researchers, and avoided spending on tools that look impressive in procurement but add nothing in production.
Data
Working with both sides of the alternative data market.
Signal & Craft works on both sides of the alternative data market. For capital markets teams, the work is about building signals that actually generate alpha. For data and technology firms, the work is about understanding the value in your data well enough to sell it credibly to a sophisticated buy-side audience.
For hedge funds and asset managers
I help research teams source, evaluate and integrate data-driven signals. That covers the full path from initial sourcing and trial design, through systematic backtesting and walk-forward validation, to integration into the live research platform. The aim is not novelty for its own sake. It is robust signals that generalise under proper testing and contribute to an investment process you can defend to investors and, importantly, to your own risk and compliance functions.
For data providers and technology firms
For firms selling data and analytics into capital markets, I help translate raw product capability into the language that quant teams and CIOs respond to. That means understanding what signal your data actually contains, how a buy-side researcher will evaluate it in practice, and the difference between fundamental and quantitative team use cases. The result is sharper positioning, better targeted outreach, and shorter sales cycles with technical buyers who tend to be unforgiving (how many times has a trial not resulted in a sale, only for you to repeat the same exercise the following year?).
Why Signal & Craft
Sitting on both sides of the table is what makes the work effective. I have licensed and integrated alternative data at scale on the buy-side leading to millions in residual profit, and I have advised data providers on how to package and present what they have. The underlying principles are the same on either side.
AI
Cutting through the noise without becoming part of it.
Every vendor in capital markets now claims to have an AI product. Everyone CXO is demanding more AI and often they cannot articulate why or what that actually means in practice.
Business directors, COOs and team leaders are increasingly asked to evaluate AI tools without a reliable way to separate genuine capability from a polished demo. The cost of getting it wrong is not only wasted budget, it is also the harder-to-measure cost of integrating a tool into a workflow and discovering, eighteen months in, that it never delivered what was promised. Or sometimes worse, it does deliver value but at a fraction of the cost and effort initially anticipated.
How Signal & Craft helps
Engagements range from short evaluations of specific tools, through to designing AI integration into research, trading or operations workflows, to advising on internal AI policy and governance. The brief is usually some combination of three questions: is this tool worth the spend, how should we integrate it, and what should our position be on the broader category.
The work is deliberately unglamorous. A meaningful share of the advice on any engagement is that a particular tool, project or vendor is not worth the money, or that the team's existing toolkit already does the job. For COOs and CFOs in particular, that candid approach is often the most valuable output, and to technical teams wanting to get their hands on the tools it is this type of advice that means they get tools in hands faster.
Why Signal & Craft
The advice is grounded in active research rather than borrowed from last year's conference circuit. I am active in the AI research community and continue to actively work in the faculty of engineering, department of computer science at UCL, a top 5 global university. Importantly, my network can be leveraged to provide insights and connections across a broader spectrum of technical disciplines.
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