Quantitative Analyst Jobs in Web3

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Job Position Company Posted Location Salary Tags

Crypto.com

Hong Kong, Hong Kong

$89k - $100k

Keyrock

London, United Kingdom

$77k - $84k

Arrakis Finance

Switzerland

$32k - $87k

Token Metrics Inc.

London, United Kingdom

$28k - $38k

Kronosresearch

Remote

$89k - $102k

Lido

Remote

$120k - $140k

Token Metrics

Austin, TX, United States

$32k - $81k

Impossible Cloud

Zug, Switzerland

$87k - $150k

Gsrmarkets

New York, NY, United States

$150k - $200k

Lobster

Paris, France

$40k - $100k

Gravity Team

Remote

$100k - $250k

G-20 Group

Zurich, Switzerland

$84k - $106k

Kronosresearch

Remote

$27k - $70k

Binance

Asia

DTG Finance & Capital Markets

New York, NY, United States

$76k - $80k

Crypto.com
$89k - $100k estimated
Hong Kong, Hong Kong SAR

Quantitative Analyst (Options)

Hong Kong, Hong Kong SAR
Trading – Trading /
Full-time /
Hybrid

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The Team
We are seeking a Quantitative Analyst to join our Trading Team. The team is responsible for market making and proprietary trading across options, structured products, and delta one products.

The Role
Front-office quant role dedicated to the options trading desk. Drive quantitative projects to enhance pricing models, risk management, trading strategies, and booking/settlement workflows. Act as the quantitative backbone for traders, ensuring accurate volatility fitting and robust backtesting, while providing technical guidance to developers.

Job Responsibilities

    • Model Implementation & Volatility Fitting: Improve volatility surface construction. Research and implement stochastic volatility models for accurate pricing and risk.
    • Strategy Backtesting & Development: Partner with traders to prototype and backtest new strategies. Analyze historical data to identify patterns and inefficiencies.
    • Project Management: Own quantitative projects end-to-end—from Python research and prototyping to productionization with developers (C++).
    • Tool Development: Build trade analysis tools, scenario simulators, and real-time risk dashboards.
    • Collaboration: Bridge the gap between traders and developers. Translate trader needs into technical specs and ensure timely delivery.
    • Post-Trade Analysis: Perform deep-dive P&L and Greek exposure analysis. Explain performance and suggest improvements.

Job Requirements

    • Master’s or PhD in a quantitative field (Mathematics, Physics, Financial Engineering, Computer Science) from a top-tier university.
    • Proven quant experience, preferably in an options market-making or derivatives prop trading firm.
    • Track record working with options theory and volatility trading.
    • Python is a must; Expert in data analysis, statistical modeling, and prototyping.
    • C++ is a strong plus; Experience with low-latency production code or close collaboration with C++ developers.
    • Familiarity with Git and collaborative coding.
    • Deep understanding of option pricing models (Black-Scholes, local vol, stochastic vol), Greeks, volatility surfaces, and common trading strategies.
    • Self-starter who drives projects independently. Strong communication skills to bridge traders and developers. Acute attention to detail and rigorous approach to data validation.
We may use artificial intelligence tools to analyze the content of your Resume/CV against the specific requirements for the position. The purpose is to support our recruitment team in reviewing applications more effectively. These tools assist our recruitment team in their evaluation of your application by providing recommendations, but they do not replace human judgment. Final hiring decisions are ultimately made by humans who consider the insights generated by the tools along with other relevant information. If you would like more details about how your personal information is processed, please contact us.
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What do quantitative analyst do?

A quantitative analyst, also known as a 'quant', is a professional who uses quantitative techniques to develop and implement financial models, analyze data, and make investment decisions

Quants are typically employed by financial institutions such as hedge funds, investment banks, and asset management firms

In Web3 quantitative analysts can leverage their expertise in data analysis and modeling to inform investment decisions and help build new decentralized systems and applications

The job of a quantitative analyst can vary depending on the employer and industry, but generally, they use mathematical and statistical models to analyze financial data and make informed investment decisions

They may use programming languages like Python, R, or MATLAB to develop these models

Some specific tasks that a quantitative analyst may be responsible for include:

  • Analyzing financial data and identifying patterns and trends.
  • Developing and testing mathematical models to predict financial market behavior.
  • Designing and implementing trading strategies based on quantitative analysis.
  • Decentralized finance (DeFi) analysis: Quants can analyze various decentralized financial protocols to identify opportunities for investment and assess risks associated with these protocols. They may develop models to predict the behavior of decentralized financial instruments and evaluate their performance.
  • Cryptocurrency market analysis: Quants can analyze cryptocurrency markets and identify patterns and trends that may be used to inform trading strategies. They can also develop models to predict the price movements of cryptocurrencies based on various factors such as supply and demand, market sentiment, and adoption rates.
  • Smart contract analysis: Smart contracts are self-executing contracts with the terms of the agreement directly written into code. Quants can analyze smart contract code to identify potential vulnerabilities and assess the risk associated with the execution of the contract.
  • Web3 data analysis: Quants can analyze data from various Web3 platforms and protocols to identify trends and make informed decisions. This may involve developing new techniques for analyzing decentralized data, such as utilizing data from on-chain transactions to gain insights into user behavior and network activity.