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Bespoke Labs

Bespoke Labs is a venture funded startup creating AI tools for data curation and post-training LLMs. (We are hiring!)

Quantitative Financial Specialist

Financial Planning and AnalysisFinancial Planning and AnalysisContractRemoteMid LevelTeam 1-10H1B No SponsorCompany SiteLinkedIn

Location

United States

Posted

8 days ago

Salary

0

Seniority

Mid Level

Postgraduate Degree2 yrs expExperience acceptedEnglishNumpyPandasPython

Job Description

Quantitative Financial Specialist

Bespoke Labs

• 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

Job Requirements

  • 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

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