Quantitative Researcher

hermeneutic Investments

. Develop and optimize systematic trading strategies and signals across digital asset markets.

Posted 9/18/2026full-timeRemote • TaiwanMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates strong quantitative and statistical reasoning, with expertise in Python programming for analyzing large datasets and building quantitative research pipelines. Capable of developing and optimizing systematic trading strategies while collaborating effectively with traders and engineers.

Highest-signal resume keywords

Python ProgrammingQuantitative ModelingStatistical AnalysisMarket Microstructure ResearchHigh-Frequency Trading Experience

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Hard Skills

Quantitative ResearchStatistical ReasoningTime-Series AnalysisBacktesting FrameworksSignal GenerationRisk ManagementEmpirical ResearchRobustness TestingData AnalysisExperimental Design

Soft Skills

Intellectual CuriosityEffective CollaborationAttention to DetailClear Communication

Industry Keywords

Digital Asset MarketsMarket MakingOrder BooksTransaction CostsShort-Horizon Price DynamicsDerivativesPerpetual FuturesHigh-Frequency Financial Data

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Develop and optimize systematic trading strategies and signals across digital asset markets.
Analyze high-frequency market data, including trades, order books, derivatives data, and cross-venue market activity, to identify exploitable market structure and behavioral patterns.
Formulate research hypotheses and design statistically rigorous experiments.
Build backtesting and simulation frameworks accounting for transaction costs, market impact, latency, liquidity, and real-world trading constraints.
Research market microstructure, liquidity dynamics, price formation, execution behavior, and short-horizon alpha.
Develop quantitative models for signal generation, execution, portfolio construction, and risk management.
Evaluate existing strategies and identify opportunities to improve alpha, execution quality, and robustness.
Collaborate with engineers to translate successful research into reliable production trading systems.
Monitor live strategy performance and investigate discrepancies between research, simulation, and production results.
Improve research methodologies, datasets, tooling, and experimental standards across the quantitative research process.
Collaborate with traders, quantitative researchers, and engineers while owning individual research.

Requirements

What you’ll need

A degree in Mathematics, Statistics, Computer Science, Physics, Engineering, Finance, or another highly quantitative field.
Strong quantitative and statistical reasoning, with the ability to translate ambiguous market questions into testable hypotheses.
Strong programming ability, particularly in Python, with experience analyzing large datasets and building quantitative research pipelines.
Experience conducting empirical research using financial, market, or similarly noisy real-world datasets.
Strong understanding of probability, statistics, time-series analysis, and quantitative modeling.
Ability to distinguish statistically interesting results from economically meaningful and tradable opportunities.
Strong attention to research methodology, including robustness testing, avoiding look-ahead bias and overfitting, and correctly evaluating out-of-sample performance.
Intellectual curiosity and the ability to independently investigate complex problems while collaborating effectively with others.
Excellent English communication skills and the ability to clearly explain research methodology, results, limitations, and implications.
Preferred: Prior experience in quantitative trading, systematic investing, market making, or high-frequency trading.
Preferred: Experience researching market microstructure, execution, order books, transaction costs, or short-horizon price dynamics.
Preferred: Experience working with tick-level or high-frequency financial data.
Preferred: Familiarity with digital asset markets, derivatives, perpetual futures, and fragmented multi-venue market structure.
Preferred: Experience developing signals or strategies that have been deployed into live trading.
Preferred: Experience with machine learning methods applied to financial markets.