Quantitative Researcher Jobs in Web3
76 jobs found
| Job Position | Company | Posted | Location | Salary | Tags |
|---|---|---|---|---|---|
Selby Jennings | New York, NY, United States | $84k - $150k | |||
Swissblock | Baar ZG | $84k - $100k | |||
Keyrock | London, United Kingdom | $72k - $90k | |||
Binance | Taipei, Taiwan |
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Confusion Capital | Remote | $180k - $220k | |||
Confusion Capital | Remote | $180k - $220k | |||
Monad Foundation | Remote | $86k - $110k | |||
Monad | New York, NY, United States | $74k - $100k | |||
Confusion Capital | Remote | $180k - $220k | |||
Numus | Remote | $105k - $120k | |||
Wintermute | London, United Kingdom | $36k - $87k | |||
Qube Research & Technologies | Remote | $64k - $85k | |||
Cyber Capital | Amsterdam, Netherlands | $105k - $108k | |||
Swissblock | Baar ZG | $90k - $100k | |||
InfiniteQuant | New York, NY, United States | $72k - $120k |
Company Overview:
Partnered with a leading digital asset management firm specializing in high-frequency and algorithmic trading strategies across the crypto markets. Our team consists of world-class quantitative researchers, engineers, and traders leveraging advanced mathematical models, data-driven insights, and cutting-edge technology to gain a competitive edge. We are seeking a Sr. QR with expertise in systematic trading strategies to drive alpha generation.
Key Responsibilities:
- Design, develop, and refine systematic trading strategies in high-frequency and algorithmic crypto markets.
- Conduct rigorous quantitative research and statistical modeling to identify market inefficiencies, arbitrage, and liquidity opportunities in centralized (CEX) and decentralized (DEX) markets.
- Develop predictive models and machine learning techniques for alpha signal generation and execution optimization.
- Collaborate with engineers to enhance backtesting frameworks, trading infrastructure, and low-latency execution systems.
- Analyze high-frequency market data, order book dynamics, and liquidity patterns to improve trading performance.
- Optimize risk-adjusted returns by implementing robust portfolio and risk management frameworks.
- Stay ahead of the evolving crypto landscape, exploring new DeFi protocols, derivative markets, and trading venues.
- Proven experience as a Quantitative Researcher in crypto trading, HFT, or systematic trading.
- Strong mathematical and statistical foundation with expertise in probability, stochastic processes, optimization, and time-series analysis.
- Programming proficiency in Python, C++, Rust, or Java for research, strategy development, and automation.
- Deep understanding of crypto market microstructure, execution algorithms, and liquidity dynamics.
- Experience with machine learning, reinforcement learning, or alternative data sources is a plus.
- Ability to work in a fast-paced, high-pressure environment and adapt to evolving market conditions.
- Ph.D. or Master's degree in a quantitative field (Mathematics, Physics, Computer Science, Finance, etc.) preferred.
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.