Quantitative Financial Specialist

Bespoke Labs

Quantitative Financial Specialist focusing on systematic trading and quantitative research with strong Python skills. Building and validating strategies for market microstructure using financial theory.

Posted 5/21/2026contractRemote • 🇺🇸 United StatesJuniorMid-LevelWebsite

Tech Stack

Tools & technologies

NumpyPandasPython

About the role

Key responsibilities & impact

Research, develop, and validate systematic trading strategies — including statistical arbitrage, momentum, mean reversion, and factor models
Write clean Python code to implement backtesting frameworks, signal generation pipelines, and execution logic with proper out-of-sample validation and transaction cost modelling
Develop quantitative trading tasks grounded in market microstructure and financial theory (e.g. alpha decay analysis, regime detection, portfolio construction under realistic constraints)
Work directly with trading infrastructure, execution systems, and risk tooling to debug and validate strategy behaviour at the portfolio level in a simulated context
Perform risk analysis including factor exposure decomposition, drawdown analysis, and stress testing across market regimes
Document research methodology, model assumptions, and backtest results to rigorous engineering and research standards

Requirements

What you’ll need

Master's or PhD in a quantitative discipline: Mathematics, Statistics, Physics, Computer Science, Financial Engineering, or similar
2–5 years of hands-on experience in quantitative research, systematic trading, or a closely related role at a hedge fund, prop shop, or asset manager
Solid understanding of financial markets, trading mechanics, and market microstructure. You should be comfortable interpreting a P&L attribution and spotting a flawed backtest
Proficiency in Python (NumPy, pandas, SciPy, statsmodels) specifically for research, backtesting, and trading system development, not general software engineering
Experience with time-series modelling, factor analysis, and statistical inference applied to financial data
Familiarity with execution concepts and market data infrastructure (order types, slippage, tick data, market impact)
Ability to build financially-grounded quantitative models rather than purely data-driven black boxes
Published research or thesis work in quantitative finance, econometrics, or a related empirical field
Background in high-frequency trading, market making, or latency-sensitive execution
Familiarity with machine learning applied to finance (gradient boosting, sequence models, reinforcement learning for execution)
Exposure to one or more of the following:
Options pricing, volatility modelling, or derivatives trading
Alternative data sourcing and signal extraction (NLP, satellite, order flow)
Portfolio optimisation under real-world constraints (transaction costs, turnover limits, risk budgets)
Crypto markets, DeFi protocols, or digital asset microstructure

Benefits

Comp & perks

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Hard Skills & Tools

Pythonstatistical arbitragemomentum tradingmean reversionfactor modelstime-series modellingfactor analysisstatistical inferencerisk analysisportfolio construction

Soft Skills

research methodologyanalytical thinkingproblem-solvingcommunicationattention to detailcollaborationdocumentation

Certifications

Master's degreePhD