Young Talent Fellowship Program

The goal of the Young Talent Fellowship Program is far beyond superficial investment skill training. It is to identify exceptional talent early and develop future portfolio managers for Calais.

We look for candidates with the core ingredients to become great investors: curiosity, judgment, ambition, resilience and a willingness to learn. We train with the expectation that our associates can grow into the investors who will one day lead our business.

The Fellowship is designed for outstanding young talents who want to work on practical, research-driven trading problems. Fellows will work on either:

1. 1A focused market research topic
2. 2A specific systematic trading strategy
3. 3A data-driven alpha research project
4. 4A derivatives, arbitrage, execution, or portfolio construction problem

The objective is not only academic research, but also practical contribution. A successful project may directly improve our market understanding, enhance existing strategies, or contribute to live trading PnL.

Selected fellows will receive access to institutional-level research and trading infrastructure, including:

• Comprehensive market and alternative datasets
• High-performance research servers
• Unlimited AI coding and research tools
• Internal research guidance from experienced quant researchers and traders
• Monthly allowance
• Opportunity to work on real trading problems with production-level impact

For outstanding projects, strategies developed during the Fellowship may be deployed into live trading. Depending on strategy quality, robustness, and capacity, the firm may allocate trading capital ranging from tens of thousands of USD to over one million USD. Fellows whose work contributes to live trading may be eligible for a meaningful PnL-sharing arrangement.

The program is primarily designed for PhD students, but we also welcome exceptional master's and undergraduate students with strong technical and research ability. Ideal candidates should demonstrate:

• Fast learning ability and strong intellectual curiosity
• Strong Python programming skills
• Practical AI coding ability, including effective use of AI tools for research and development
• Familiarity with Linux-based research and development environments
• Solid data analysis, statistical modeling, and quantitative research skills
• Ability to conduct deep research independently
• Strong interest in financial markets, trading, derivatives, or systematic strategies

Prior trading experience is not strictly required, but strong technical execution, research discipline, and market curiosity are essential.

• CMU

  Prior internship at Point72. Successfully improved an existing arbitrage strategy that was later deployed into production with approximately USD 10 million trading allocation.

• HKU

  Prior internship experience at Jane Street.

• UC Berkeley

  AI PhD candidate with strong machine learning and research background.

These examples are not strict requirements. We are open to candidates from diverse academic backgrounds as long as they show exceptional learning ability, technical strength, and research potential.

• Cross-asset systematic trading strategies: Equity, ETF, futures, options, FX, commodities, and digital assets
• Statistical arbitrage and relative value trading
• Derivatives pricing, hedging, and volatility strategies
• Execution algorithms and market microstructure research
• Macro-driven systematic strategies
• Alternative data and factor research
• Portfolio construction, risk allocation, and strategy monitoring
• AI-assisted quantitative research workflows

1. 01

   CV Review

   Assessment of academic background, technical skills, research experience, and market interest.

2. 02

   Take-Home Project & Presentation

   Candidates complete a research or coding project and present their methodology, findings, limitations, and possible production path.

3. 03

   Coding & Math / Statistics Interview

   Evaluation of Python ability, data analysis skills, statistical reasoning, and quantitative problem-solving capability.

The Fellowship is intended to be highly practical. Strong fellows may have the opportunity to:

• Work closely with experienced quant researchers and traders
• Build research that can directly influence live trading
• Contribute to production-level strategies
• Receive significant upside through PnL-sharing if their work generates real trading value
• Potentially continue with the firm in a longer-term internship, research, or full-time role

We welcome students who are intellectually ambitious, technically strong, and motivated to solve real problems in global financial markets.