Hiring a first quant: sequence matters
A first quant hired before there is data engineering to support them spends a year building pipelines instead of models. The desk pays research rates for plumbing, the quant gets bored doing work they did not sign up for, and the models the hire was justified by arrive a year late if the person stays long enough to build them.
The failure comes from a reasonable-sounding shortcut. The desk wants forecasting or signals, so it hires the person whose title says forecasting and signals, and assumes the prerequisites will get sorted along the way. They do get sorted, by the quant, slowly, resentfully, and usually worse than a data engineer would have sorted them, because pipeline reliability is its own craft and researchers are not selected for it.
The sequence that works is data, then research, then execution. First someone who fetches, cleans and historises everything the desk touches: prices, order books, forecasts, outages, the asset's own telemetry if there is one. Then the researcher, who now starts productive in week two because the raw material exists. Then, when there is something worth automating, the execution build, which by that point can be scoped against strategies that exist rather than strategies that are hoped for.
Two honest complications. Small desks sometimes cannot fund the sequence, in which case the right first hire is the rarer hybrid who has done both data and research and knows which one comes first. That person exists but commands a premium, and the brief should say plainly that the first year is infrastructure. Hiring a pure researcher and hoping is not a cheaper version of this plan. It is a slower version of not having one.
The other complication is that the sequence has a political cost: the first hire is the least glamorous one, and boards want to hear about models. The answer to that is arithmetic. A researcher on top of working data produces in months what a researcher on top of nothing produces in years, and the cheapest time to learn this is before the first payslip rather than at the first annual review.
Case notes, desk-build maps and real moves across European power.
Asset-backed and battery optimisation roles keep getting scoped as trading roles and then filled with traders who are bored within a year. The desk writes the brief in good faith: it sees intraday markets, prices, a P&L, so it writes a trading job.
Country-by-role pool sizes answer a question that headcount totals cannot: not where the most power professionals are, but where the specific people you need are actually concentrated.
Desks spent the year hiring against volatility rather than against growth. The distinction matters because the two produce different vacancies.
Senior power trading, quant and optimisation hires across European markets.