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Quantitative Research · Internship

Quantitative Research Intern at Evore Labs.

Work alongside our researchers on the methods by which our systems form, test and retire hypotheses about markets.

Stipend
£1,300 / month
Duration
2 months
Work
Remote
Location
Remote-first · UK
The Role

What you will own.

You will join the research pod that designs how our systems form and judge ideas about markets. Rather than being handed a dataset and a target, you will help shape the questions themselves — turning vague market intuition into precise, falsifiable hypotheses, then building the experiments that decide whether they survive. Expect to read widely, argue about methodology, write careful code, and defend conclusions with evidence. Your work feeds directly into how the lab decides what is real.

Responsibilities

  • Translate market observations into precise, falsifiable hypotheses with explicit success criteria.
  • Design and run backtests and out-of-sample experiments that resist overfitting, lookahead and survivorship bias.
  • Build reproducible research notebooks and pipelines others can rerun and trust.
  • Quantify uncertainty honestly — confidence intervals, multiple-testing corrections, regime sensitivity.
  • Write clear research memos and present in reviews, including the ideas that failed.

Qualifications

  • Strong foundation in probability, statistics or time-series analysis.
  • Comfortable writing clean, tested Python with NumPy and pandas.
  • Evidence of rigorous empirical work — a thesis, competition, paper or serious project.
  • Able to reason about bias, variance and why a backtest might be lying to you.
  • Clear written and verbal scientific communication.

Nice to have

  • Exposure to market microstructure, asset pricing or econometrics.
  • Experience with large panel or tabular datasets.
  • Contributions to open-source or published research.
Learning

What this track teaches.

You will learn

  • How a research organism separates real signal from noise at scale.
  • Experiment design that survives contact with live markets.
  • The statistical failure modes specific to financial data.
  • How validated ideas become governed, production signals.
Tools

The stack around the work.

Python
NumPy / pandas
Polars
Jupyter
statsmodels
DuckDB

Apply

Send the work that proves the fit.

Tell us what you have built, tested or discovered. A researcher or engineer reads every application.

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