Quantitative Analyst, Electronic Trading

KeyBank•New York, NY

1d•Hybrid

About The Position

The Quantitative Analyst will sit within the electronic trading function and will be responsible for research, development, and implementation of quantitative models and algorithmic enhancements that improve execution quality. The role requires the ability to translate research into production-facing logic used by equity trading algorithms, while also building robust post-trade analytics used to diagnose performance and guide iteration.

Requirements

Strong understanding of US equity trading and market microstructure.
Knowledge of algorithmic trading concepts (order books, volume forecasting, impact cost modeling).
3-5 years experience in a trading environment developing execution algorithms or high-frequency trading strategies.
Demonstrated experience applying statistical modeling and machine learning to large datasets (market, orders, executions).
Strong quantitative skills and hands-on proficiency in Python and/or R for research and production analytics.
Object-oriented programming skills (Java, C++) for integrating models/signals into execution systems.
Proven ability to communicate clearly (verbal/written), including client-facing discussions and contributing to research write-ups/white papers.
Ability to manage multiple concurrent priorities in a fast-paced environment.
Demonstrated commitment to a diverse workforce and inclusive collaboration.

Nice To Haves

Experience with Google Cloud Platform a plus.

Responsibilities

Research, design, and implement enhancements to trade scheduling, order placement, and order routing logic used across equity trading algorithms, with direct responsibility for taking ideas from analysis into production.
Develop and deploy new execution algorithms and quantitative approaches, including machine-learning techniques, to improve execution quality across varying market conditions and client objectives.
Analyze large, complex datasets (market data, orders, executions) to evaluate algorithm performance, identify drivers of outperformance or underperformance, and develop signals and diagnostics that guide continuous improvement.
Build, maintain, and enhance post-trade performance and market-impact models, incorporating realized market conditions and separating algorithm, broker, and market effects in order to produce actionable insights.
Design and maintain momentum- and regime-aware execution logic, including determining when orders should utilize different execution styles (e.g., Implementation Shortfall vs. VWAP) based on expected momentum or reversion dynamics.
Perform tactic-level and venue-level attribution, isolating the impact of execution tactics and venue selection on performance, and supporting curation decisions with statistically sound analysis rather than topline A/B results alone.
Evaluate the impact of algorithm changes and parameter updates using causal inference techniques (e.g., matching, synthetic control, or propensity-based approaches) to properly measure treatment effects and avoid biased conclusions.
Develop and maintain simulation frameworks used to test algorithm behavior, allocation logic, and performance sensitivity across different market regimes prior to rollout.
Contribute to and evolve allocation and optimization frameworks (including exploration vs. exploitation concepts such as bandit-style approaches) that dynamically adapt routing and strategy usage based on observed performance.
Implement bespoke algorithms and analytics in response to client and desk needs, balancing research rigor with practical execution requirements.
Support internal and external stakeholders by translating quantitative findings into clear, client-ready explanations, performance reviews, and written materials, including white papers or technical summaries when appropriate.
Operate effectively in a fast-paced trading environment, managing multiple concurrent initiatives while maintaining strong controls, documentation, and analytical discipline.