Quantitative Researcher

Quantitative Researcher/Analyst

Location: Remote

Firm Overview

Harrington Starr has partnered with a systematic trading firm building a platform designed to operate across multiple prediction market exchanges. The prediction market space has grown explosively over the past year and is forecasted to continue scaling significantly. Institutional participation is still nascent, structural inefficiencies are large, and the opportunity is significant for a small, well-equipped team.

The firm is a small, hands-on team building a next-generation trading platform from the ground up, with a strong emphasis on AI-augmented development. The team works side-by-side with AI coding agents as a core part of the workflow, focusing on the quality of ideas, rigor of analysis, and judgment behind the work while delegating much of the implementation to agents. Candidates who are excited about this approach will thrive here.

Position Overview

The firm is seeking a Quantitative Researcher/Analyst to investigate, evaluate, and validate trading opportunities on prediction markets. This is a research role, not a trading or systems-engineering role: you will source and assess new data, design and run both backward-looking and prospective studies, and produce clear, honest reads on whether a given edge is real, durable, and worth building.

The ideal candidate has 4–10 years of experience, a solid mathematical foundation, and a track record of research that led to strategies or models which were actually implemented. You will write your own code for research and prototyping and lean on AI tooling to move quickly, without depending on an engineering team for every study.

Responsibilities

• Identify, source, and evaluate new data sources for fair value, signal generation, and edge discovery.
• Design and run backward-looking studies (backtests, historical analyses, robustness and decay checks) and prospective studies to assess whether a candidate approach has a real, durable edge.
• Produce clear research write-ups with honest go/no-go conclusions: what the edge is, how big it is, how confident we should be, and what would need to be true for it to survive in production.
• Build lightweight proof-of-concept artifacts to demonstrate and validate ideas. You prototype, a platform team productionizes.

Qualifications

• 4–10 years of experience in an applied quantitative research or analysis role, with a track record of empirical work that informed real decisions
• Solid mathematical foundation: probability, statistics, and basic ML, with strong intuition for experiment design, sampling, overfitting, and what makes a result trustworthy.
• Demonstrated experience researching strategies or models that were productized and implemented, ideally with measurable success. Research that shipped and worked matters more than publications or pedigree.
• Hands-on fluency with data: SQL, Pandas, and analytical warehousing technologies (e.g., Snowflake). Comfortable wrangling messy, real-world data independently.
• Able to write Python for research and prototyping without requiring engineering support to run a study or stand up a proof of concept.

Preferred Qualifications

• Experience with prediction markets, sports betting markets, or event contract venues.
• Familiarity with market-making and liquidity provision concepts: quoting, inventory, spread capture, and adverse selection.
• Experience with statistical analyses common in trading research: markout distributions, regime detection, fill-quality measurement, and P\&L attribution.

If this looks interesting, or you'd like to discuss similar roles, please apply directly or reach out to [email protected]