Job Overview
Location
Remote
Job Type
Contract
Category
Software Engineering
Date Posted
May 22, 2026
Full Job Description
đź“‹ Description
- • 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 out-of-sample validation and transaction cost modelling
- • Develop quantitative trading models grounded in market microstructure and financial theory, such as alpha decay analysis, regime detection, and portfolio construction under realistic constraints
- • Work directly with trading infrastructure, execution systems, and risk tooling to debug and validate strategy behavior at the portfolio level in a simulated environment
- • Perform risk analysis including factor exposure decomposition, drawdown analysis, and stress testing across different market regimes
- • Document research methodology, model assumptions, and backtest results to rigorous engineering and research standards
- • Interpret P&L attribution and identify flawed backtests through deep understanding of trading mechanics and financial markets
- • Implement financially-grounded quantitative models rather than purely data-driven black boxes
- • Utilize Python libraries including NumPy, pandas, SciPy, and statsmodels for research, backtesting, and trading system development
- • Apply time-series modelling, factor analysis, and statistical inference specifically to financial datasets
- • Engage with execution concepts such as order types, slippage, tick data, and market impact in model design
- • Leverage market data APIs including Bloomberg, Refinitiv/LSEG, or equivalent for empirical validation
- • Use version control (Git) and SQL for data management and model tracking
- • Employ cloud platforms (AWS, GCP, Azure) and workflow orchestration tools (Airflow, Prefect) where applicable to scale research workflows
- • Build and maintain trading systems that integrate realistic constraints such as transaction costs, turnover limits, and risk budgets
- • Analyze alternative data sources including NLP, satellite imagery, and order flow for signal extraction when applicable
- • Evaluate options pricing, volatility modelling, and derivatives trading dynamics in strategy design
- • Investigate crypto markets, DeFi protocols, or digital asset microstructure as part of strategy exploration
- • Collaborate with trading and risk teams to ensure models align with operational and regulatory realities
🎯 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, including ability to interpret P&L attribution and spot flawed backtests
- • Proficiency in Python (NumPy, pandas, SciPy, statsmodels) for research, backtesting, and trading system development
- • 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)
🏖️ Benefits
- • Remote work arrangement
- • Access to market data APIs including Bloomberg and Refinitiv/LSEG
- • Use of cloud platforms (AWS, GCP, Azure) and workflow orchestration tools (Airflow, Prefect)
- • Opportunity to work with alternative data sources such as NLP, satellite imagery, and order flow
- • Exposure to crypto markets, DeFi protocols, and digital asset microstructure
- • Environment focused on financially-grounded modelling rather than purely data-driven approaches
Skills & Technologies
See exactly how your profile matches this role — strengths, skill gaps, and what to do about them.
About BespokeLabs Inc.
BespokeLabs Inc. builds AI-driven software that personalizes digital experiences at scale. The platform ingests first-party user data to generate real-time content, messaging and product recommendations for e-commerce, media and SaaS companies, optimizing engagement and revenue without manual segmentation. Customers deploy lightweight JavaScript or server-side SDKs, configure goals and privacy controls, and receive continuously learning models that adapt to each visitor. Founded in 2019, the San Francisco–based company serves mid-market to enterprise clients across North America and Europe.
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