Associate Quantitative Trader

Torchcast AI; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

Torchcast AI in Singapore is seeking a curious, rigorous quantitative trader to turn research ideas into executable strategies across markets. The role covers opportunity discovery, hypothesis design, data analysis, backtesting, execution, and live performance review.

This associate-level position welcomes candidates with strong quantitative foundations; prior full-time trading experience is not required as internships, competitions, and independent projects may be considered.

Qualifications

• Master's or PhD in a quantitative discipline is preferred; exceptional candidates with equivalent research or trading experience are welcome.
• Strong grounding in probability, statistics, linear algebra, time-series analysis, optimisation, and experimental design.
• Ability and genuine interest in independently finding trading opportunities, testing them sceptically, and designing practical strategies.
• Strong Python skills and comfort working with data and research code; SQL is useful, and C++ or another performance-oriented language is a plus.
• Hands-on AI or machine-learning experience is preferred, together with an understanding of leakage, non‑stationarity, explainability, and model risk.
• Sound judgement, intellectual honesty, attention to detail, and respect for risk controls and confidential information.
• Clear written and verbal communication in a collaborative environment.

Responsibilities

• Discover opportunities.
• Design strategies.
• Research with discipline.
• Apply AI thoughtfully.
• Move from research to trading.
• Own risk.
• Communicate clearly.

Skills

Python

SQL

C++

Probability

Statistics

Time-series

Optimization

Research design

Machine learning

Communication

Education

Master's or PhD in quantitative discipline

Tools

Python

Job description

About Torchcast

Torchcast builds AI systems that forecast real-world outcomes.

Our platform combines forecasting-trained models, evidence retrieval, quantitative methods, and probabilistic reasoning to help organizations make better decisions under uncertainty. We work across financial markets, economics, supply chains, technology, and other complex domains.

The opportunity

We are seeking a curious, rigorous quantitative trader who enjoys finding overlooked trading opportunities and turning research ideas into robust, executable strategies. The role spans opportunity discovery, hypothesis design, data analysis, backtesting, execution, and live performance review.

This is an Associate-level position suited to candidates with strong quantitative foundations and evidence of hands-on research ability. Prior full-time trading experience is not required; relevant research, internships, competitions, and independent projects may be considered.

What you will do

• Discover opportunities. Generate and prioritize hypotheses from market structure, price behaviour, cross-asset relationships, alternative data, and changing market regimes.
• Design strategies. Translate hypotheses into systematic strategies with clear signals, portfolio construction, entry and exit logic, risk limits, and capacity assumptions.
• Research with discipline. Build reproducible datasets and backtests; control for leakage, overfitting, selection bias, transaction costs, slippage, latency, and market impact.
• Apply AI thoughtfully. Use machine learning or AI-assisted workflows for signal discovery, forecasting, regime detection, feature extraction, alternative-data analysis, optimisation, or research automation where they add measurable value.
• Move from research to trading. Define paper-trading and deployment criteria, monitor live behaviour, investigate deviations, and improve strategies through evidence rather than hindsight.
• Own risk. Evaluate drawdowns, tail scenarios, concentration, liquidity, model instability, and operational failure modes before and after launch.
• Communicate clearly. Present assumptions, results, limitations, and trade-offs to traders, researchers, and engineers in a concise, decision-ready form.

What we are looking for

• Master's or PhD in a quantitative discipline is preferred; exceptional candidates with equivalent research or trading experience are welcome.
• Strong grounding in probability, statistics, linear algebra, time-series analysis, optimisation, and experimental design.
• Ability and genuine interest in independently finding trading opportunities, testing them sceptically, and designing practical strategies.
• Strong Python skills and comfort working with data and research code; SQL is useful, and C++ or another performance-oriented language is a plus.
• Hands‑on AI or machine‑learning experience is preferred, together with an understanding of leakage, non‑stationarity, explainability, and model risk.
• Sound judgement, intellectual honesty, attention to detail, and respect for risk controls and confidential information.
• Clear written and verbal communication in a collaborative environment.

Research or trading track record

A track record is preferred, but it is not a mandatory screening requirement. We value a transparent research process as much as headline returns. Evidence may include live or paper trading, a research portfolio, competition results, a strategy tear sheet, or a well‑documented project.

Where available, please state:

• whether results are live, paper, backtested, or independently verified;
• the evaluation period, instruments, turnover, fees, slippage, and capacity assumptions;
• return, volatility, drawdown, hit rate, and relevant risk‑adjusted measures;
• your personal contribution, key failure cases, and what you changed after review.

Sensitive account details may be anonymised. Please do not submit proprietary information belonging to a current or former employer.

What success looks like

• Good questions, not just more models. You identify opportunities with plausible economic or behavioural foundations.
• Robust evidence. Your research survives realistic costs, out‑of‑sample testing, alternative specifications, and critical review.
• Practical implementation. You can turn a promising signal into a risk‑aware strategy that can be monitored and improved in production.
• Responsible use of AI. You use AI to improve the quality or speed of research without obscuring assumptions or weakening controls.

Fair employment

Selection is merit‑based and focuses on role‑related skills, experience, and ability to perform the work. We welcome candidates from diverse backgrounds. Candidates should be able to work from Singapore; any immigration sponsorship is subject to applicable requirements and the employer's hiring policy.