Electronic Trading Quant Researcher - DarkSOR and Liquidity
* understand existing routing engines
* integrate models
* prototype components that may later be productionized
* collaborate tightly with engineering
- Develop models for fill probability, time to fill, adverse selection, and short‑horizon alpha specific to dark and conditional venues.
- Build mathematical frameworks for dark‑venue selection, probabilistic allocation, and optimization under uncertainty.
- Conduct deep microstructure research on dark pools, conditional liquidity networks, midpoint books, and hidden order workflows.
- Derive decision rules and routing policies grounded in stochastic control, dynamic programming, or Bayesian decision processes.
- Build and maintain event‑driven simulators that approximate venue matching logic and multi‑venue routing behavior.
- Prototype routing logic, models, and analytical tools using Python for research and C++/Java where fidelity to production behavior is needed.
- Analyze large‑scale historical order‑route data sets to validate model assumptions and measure routing performance.
- Run statistically sound A/B experiments to evaluate new policies and interpret outcomes rigorously.
- Read and understand the existing C++ or Java code paths used by the smart order router to ensure research aligns with live system behavior.
- Develop lightweight model components or logic prototypes in C++/Java when Python is insufficient to reproduce timing, sequencing, or state transitions.
- Collaborate with engineering to translate research insights into production code; contribute model logic, formulas, and algorithmic structure.
- Validate production implementations by comparing model outputs, execution paths, and empirical fill outcomes to research benchmarks.
- Maintain a research codebase that mirrors key parts of the production routing engine to allow accurate simulation.
- Work closely with traders, algo PMs, and product to define research questions and explain results.
- Provide clear model documentation for internal model risk and compliance.
- Partner with engineering to ensure models are implemented correctly and monitored appropriately - without owning production systems.
- PhD strongly preferred in applied mathematics, engineering, operations research, statistics, or related fields.
- Deep knowledge of stochastic control, stochastic processes, dynamic optimization, and statistical learning.
- Familiarity with microstructure‑relevant numerical methods (e.g., hazard models, PIDE formulations, survival processes, Bayesian updating).
- Strong Python skills for research modeling, prototyping, simulation, and data analysis.
- Working proficiency with C++ and/or Java to:
* implement research variants of production routing components
* explore sensitivity of routing logic
* replicate execution paths and venue interaction dynamics
* collaborate effectively with engineering teams - Ability to work with large time‑series datasets via SQL, kdb/q, or similar systems.
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