| Job Position | Company | Posted | Location | Salary | Tags |
|---|---|---|---|---|---|
Binance | Taipei, Taiwan |
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Swissblock | Baar ZG | $84k - $150k | |||
Dvtrading | Remote | $84k - $120k | |||
Okx | Remote | $140k - $300k | |||
| Learn job-ready web3 skills on your schedule with 1-on-1 support & get a job, or your money back. | | by Metana Bootcamp Info | |||
Fionics | New York, NY, United States | $36k - $100k | |||
Keyrock | London, United Kingdom | $64k - $125k | |||
Yuma | Stamford, CT, United States | $105k - $125k | |||
Dvtrading | Remote | $84k - $100k | |||
Wintermute | London, United Kingdom | $72k - $99k | |||
Wormhole Labs | Remote | $84k - $150k | |||
Gravity Team | Remote | $120k - $240k | |||
Calyptus | New York, NY, United States | $84k - $90k | |||
Theo | New York, NY, United States | $105k - $125k | |||
Injective | New York, NY, United States | $36k - $75k | |||
Fuse Energy | London, United Kingdom | $84k - $85k |
Junior Quantitative Researcher (Fresh STEM PhD graduates are welcome)
We are building out a new research function at the intersection of artificial intelligence and quantitative trading to improve the efficiency of execution algo models and more, and we are looking for a Junior Quantitative Researcher to be a founding member of this effort. You will work alongside senior quants, engineers, and traders to design AI-driven workflows that generate alpha signals, diagnose model and PnL behavior, and deepen our understanding of market microstructure.
This is a high-ownership role suited to someone who is genuinely excited about markets, has a strong research background, and is already building with modern AI tooling — including LLM-based agents. We are open to hiring at the fresh-PhD level, provided you can demonstrate research depth and a real interest in trading.
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Responsibilities
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Signal research and construction. Develop, test, and productionize predictive signals across asset classes using a combination of statistical methods, machine learning, and AI agent–driven research workflows. Take ideas from hypothesis through backtest, validation, and deployment.
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Root cause analysis (RCA). Investigate model behavior, signal decay, PnL attribution, and unexpected trading outcomes. Build tools — including agentic ones — that accelerate diagnosis and shorten the loop between observation and fix.
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Market microstructure research. Study order book dynamics, execution costs, liquidity, and venue behavior to inform both signal design and execution strategy.
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AI agent infrastructure for research. Help design and extend internal agentic systems that automate parts of the research pipeline — data exploration, hypothesis generation, backtest configuration, results summarization, and report drafting.
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Collaborate broadly. Work closely with traders, engineers, and other researchers to turn ideas into live, monitored strategies.
Requirements
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PhD (recently completed or near completion) in a quantitative field — e.g., Computer Science, Machine Learning, Statistics, Physics, Mathematics, Electrical Engineering, Operations Research, or a related discipline.
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Strong programming skills in Python; comfortable with the modern data and ML stack (NumPy, pandas, PyTorch or JAX, etc.).
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Hands-on experience building with AI agents and LLM-based systems — for example, tool-using agents, multi-step reasoning pipelines, retrieval systems, or evaluation frameworks. We want to see that you have actually built things, not just read papers.
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Solid grounding in statistics, probability, and machine learning, with the rigor to know when a result is real and when it isn't.
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Genuine interest in financial markets and trading, demonstrable through coursework, personal projects, competitions, internships, or self-directed study.
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Strong written and verbal communication; able to explain technical work clearly to a mixed audience.
Nice to Have
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Prior internship or research experience at a hedge fund, prop trading firm, market maker, bank, or fintech.
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Exposure to market microstructure, limit order books, or high-frequency data.
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Experience with backtesting frameworks, time-series analysis, or causal inference.
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Familiarity with low-latency systems, or large-scale data infrastructure.
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Publications, open-source contributions, or trading competition results.
What does a Quantitative Researcher do?
A Quantitative Researcher is a professional who conducts research in the field of finance, economics, or related fields using quantitative methods such as statistical analysis and mathematical modeling
They typically work in the financial industry, including investment banks, hedge funds, and asset management firms
The job of a Quantitative Researcher can vary depending on the employer and industry, but generally, they use quantitative techniques to analyze financial data and develop models that can be used to make investment decisions
A Quantitative Researcher in Web3 is a professional who conducts research using quantitative methods in the context of decentralized finance (DeFi) and other Web3 applications
They work to understand the behavior of decentralized systems and develop models that can be used to optimize investment strategies
Some specific tasks that a Quantitative Researcher may be responsible for include:
- Developing models for decentralized finance: Quantitative Researchers in Web3 may develop mathematical models and algorithms that can be used to analyze decentralized financial systems, such as decentralized exchanges (DEXs), lending protocols, and prediction markets. These models may be used to assess risk, predict market behavior, and optimize investment strategies.
- Conducting on-chain data analysis: Quantitative Researchers in Web3 analyze on-chain data from decentralized platforms to understand user behavior and network activity. They may use statistical techniques such as regression analysis and machine learning to analyze this data and identify patterns that can be used to inform investment strategies.
- Writing research reports: Quantitative Researchers in Web3 write reports summarizing their research findings and recommendations for investment strategies. These reports may be used by traders, portfolio managers, and other decision-makers within the organization.
- Collaborating with other teams: Quantitative Researchers in Web3 may work closely with other teams within the organization, such as developers, quants, and traders, to develop and implement investment strategies that leverage their research insights.