Stash Talent Services · 11 hours ago
Quant Developers & Data Scientists-W2 Only
Manhattan, NY
Full-time
Hybrid
Mid, Senior Level
$70/hr - $80/hrStash Talent Services is seeking highly technical Data Scientists & Quant Developers to design, build, and productionize quantitative and data-driven solutions for trading, risk, and financial analytics platforms. The role involves developing and maintaining quantitative models and analytics, collaborating with various teams, and ensuring model performance through monitoring and enhancements.
Staffing & Recruiting
Responsibilities
Design, develop, and maintain quantitative models and analytics for trading, pricing, risk, and forecasting
Implement statistical, machine learning, and signal-based models and convert research prototypes into production-grade systems
Build and optimize data pipelines for large-scale market and financial datasets
Develop time-series, regression, classification, and simulation models
Implement back-testing, scenario analysis, and performance measurement frameworks
Write efficient, scalable, and well-tested code used in research and live trading environments
Collaborate with traders, quantitative researchers, and engineering teams to deliver end-to-end solutions
Monitor live models, evaluate drift, and continuously enhance model performance
Clearly document model logic, assumptions, and technical design
Qualification
Required
Strong programming experience in Python (NumPy, Pandas, SciPy, scikit-learn)
Solid understanding of software engineering fundamentals (data structures, algorithms, design patterns)
Strong foundation in statistics, probability, linear algebra, and numerical methods
Experience with time-series analysis and financial data modeling
Proficiency with SQL and large datasets
Experience with Git, CI/CD, testing, and production support
Hands-on experience building or supporting trading, pricing, or risk models
Knowledge of financial instruments (equities, FX, fixed income, derivatives)
Understanding of back-testing methodologies, PnL attribution, Sharpe, drawdown, and risk metrics
Exposure to market microstructure, factor models, or systematic strategies
Experience with PyTorch or TensorFlow
Distributed data processing using Spark or similar frameworks
Model deployment using APIs, Docker, Kubernetes, or cloud platforms (AWS/GCP/Azure)
Familiarity with MLOps, model monitoring, and lifecycle management
Company
Stash Talent Services
2-10 employees
Funding
Current Stage
Early StageCompany data provided by crunchbase