Varsity Holdings – Quant Researcher – US Equities / AI-Driven Systematic Trading

We are building an AI-first quantitative trading and research platform focused on systematically discovering and executing trading strategies. Our trading arm runs fully automated strategies in US equities, with a focus on small/mid-cap inefficiencies, intraday trading and data-driven edge discovery. We combine proprietary backtesting infrastructure, quantitative research workflows and AI-assisted (multi-agent) strategy development.

Quant Researcher (Experienced) – US Equities / AI-Driven Systematic Trading

Work arrangement: Fully on-site at 144 Robinson Road, Singapore 068908 (no hybrid).
Employment: Full-time / permanent. Start: Immediate. Minimum 6 months.

About the Role
We are looking for an experienced Quant Researcher to generate, test and deploy systematic trading strategies. You will own the research cycle end to end, from hypothesis and data to backtest, validation and handover to live trading, working closely with our AI engineers and trading operations team.

What You Will Do
- Research and develop systematic alpha signals and strategies, primarily intraday US equities (small/mid-cap)
- Build and maintain factor libraries; evaluate signals for predictive power, decay, capacity and turnover
- Design robust backtests on our proprietary infrastructure, accounting for transaction costs, slippage, borrow and liquidity
- Guard against overfitting with out-of-sample, walk-forward and statistical significance testing
- Analyse live trade logs and execution quality; diagnose live vs. backtest divergence
- Work with AI engineers to turn research workflows into agentic pipelines (factor discovery, regime detection, strategy induction)
- Define risk limits and position sizing for strategies moving to production
- Document research clearly so ideas move quickly from prototype to production

Requirements
- 1+ years of hands-on quantitative research or systematic trading experience (industry, prop desk, fund, or a serious independent track record)
- Degree in a quantitative field (Mathematics, Statistics, Physics, Computer Science, Financial Engineering, or similar)
- Strong Python (pandas, NumPy, SciPy, statsmodels / scikit-learn)
- Solid statistics and time-series analysis: regression, hypothesis testing, multiple-testing awareness
- Proven experience building and critiquing backtests, and knowing why most of them lie
- Good understanding of market microstructure and US equity markets
- Able to work independently and communicate findings concisely

Nice to Have
- Experience with intraday / high-frequency equity data (tick, L1/L2, borrow/short data)
- ML for alpha (gradient boosting, deep learning) with sound validation practice
- Experience with LLMs or agentic frameworks (LangChain, AutoGen, CrewAI) applied to research
- SQL, cloud compute, distributed backtesting (Ray, Airflow, Prefect)
- Exposure to options, futures or crypto
- C++ or other performance languages

What We Offer
- Work at the frontier of AI x quant trading, with real capital and live production systems
- Direct impact: your research goes live and you see the results
- Fast-paced, iterative environment with a small senior team
- Competitive base salary based on experience plus performance bonus
- Proprietary data and backtesting infrastructure

How to Apply
Please apply by submitting your text CV through InternSG. In your summary, briefly describe one strategy or research project you built (data, method, how you validated it). Only shortlisted candidates will be notified.