AI Research Scientist - Front Office Systematic Team

Mondrian Alpha; New York (NY); On-site; USD 180,000 - 260,000; Full time

Job summary

Mondrian Alpha is seeking a quantitative researcher to join the front-office systematic team, embedded in the research and signal-generation process. Your work informs live capital allocation and can directly influence trading decisions.

You will design, backtest, and productionize models, collaborate with engineers and PMs, and stay abreast of applied ML/AI research to bring practical insights to the fund.

Qualifications

• PhD or equivalent research experience in a relevant field.
• Hands-on ML/AI experience applying techniques to real data.
• Strong Python coding ability to implement research.
• Comfort with ambiguity and a research process with no guaranteed answer.

Responsibilities

• Research, design, and test novel modeling approaches for signal generation and alpha discovery.
• Translate research ideas into production-ready models in collaboration with engineering.
• Rigorously backtest and validate hypotheses before they reach live trading.
• Partner directly with portfolio managers to understand what questions are worth answering.
• Stay current with applied ML/AI research and bring relevant techniques into the fund’s process.

Skills

Strong quantitative background

Hands-on ML/AI experience

Python coding ability

Comfort with ambiguity

Education

PhD or equivalent

Tools

Python

Job description

This role sits on a front office systematic team, embedded directly in the research and signal-generation process rather than supporting it from a distance. The work touches live capital allocation: models and research this person produces can end up directly informing how the fund trades.

What You’ll Do

• Research, design, and test novel modeling approaches for signal generation and alpha discovery
• Translate research ideas into production-ready models in collaboration with engineering
• Rigorously backtest and validate hypotheses before they reach live trading
• Partner directly with portfolio managers to understand what questions are worth answering
• Stay current with applied ML/AI research and bring relevant techniques into the fund’s process

Must-haves

• Strong quantitative background (PhD or equivalent research experience in a relevant field)
• Hands-on experience applying ML/AI techniques to real, messy data, not just academic benchmarks
• Strong coding ability in Python (or equivalen) to implement your own research
• Comfort with ambiguity and a research process with no guaranteed answer

Nice-to-haves

• Prior experience in systematic trading or quantitative finance
• Published research in top ML/AI venues
• Experience with time series, NLP, reinforcement learning, or relevant domains
• Experience working directly with traders or PMs on live research questions