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Coco Panda
Quantitative Software Engineer
PROFESSIONAL SUMMARY
Quantitative Software Engineer with 5 years of experience building pricing, risk, and market data systems for trading teams. Strong background in Python, C++, statistical modeling, and low-latency data pipelines. Delivered production tools that improved backtesting speed, model validation quality, and trader decision support.
PROFESSIONAL SKILLS
Quantitative Engineering
PythonC++JavaNumerical MethodsMonte Carlo Simulation
Markets and Modeling
EquitiesOptions PricingFixed IncomeRisk AnalyticsStatistical Modeling
Data and Infrastructure
SQLPandasNumPyPyTorchLinux
PROFESSIONAL EXPERIENCE
Hudson River Trading Analytics
Trading TechnologyCapital Markets
New York, NY
Quantitative Software Engineer
PythonRisk Systems
Jun 2022 - Present
- Built Python and C++ services for intraday risk calculations, reducing portfolio exposure refresh time from 14 minutes to under 4 minutes.
- Partnered with quantitative researchers to productionize factor models, improving signal deployment reliability and cutting release defects by 31%.
- Designed SQL and Pandas validation workflows for market data feeds, identifying stale quote issues before trading impact in 97% of cases.
- Optimized Monte Carlo simulation libraries using multiprocessing and vectorized NumPy routines, increasing scenario throughput by 42% on shared compute nodes.
Apex Quant Solutions
FinTechAlgorithmic Trading
Chicago, IL
Quantitative Developer
C++Backtesting
Jul 2020 - May 2022
- Developed backtesting tools for equities strategies in Python, enabling researchers to evaluate 120,000 daily simulations with reproducible experiment tracking.
- Implemented C++ pricing components for options analytics, lowering batch valuation latency by 35% during end-of-day risk processing.
- Built automated data quality checks for corporate actions and reference data, reducing research dataset exceptions by 28% over two quarters.
- Collaborated with traders and DevOps engineers to deploy Linux-based analytics jobs, improving overnight pipeline completion rate from 89% to 98%.
SELECTED PROJECTS
Derivatives Pricing and Simulation Engine - Quantitative Software Engineer
Nov 2023 - Mar 2024
- Built a Python and C++ Monte Carlo engine for option pricing, reducing simulation runtime by 38% through vectorization and parallel execution.
- Added validation reports comparing model outputs against market quotes, improving exception detection and reducing manual review time by 45%.
Market Microstructure Backtesting Framework - Lead Developer
Apr 2023 - Aug 2023
- Developed an event-driven research pipeline processing five years of tick data, enabling faster strategy iteration across equities and futures datasets.
- Implemented transaction cost modeling and walk-forward testing, improving estimated live performance alignment by 22% during pre-production review.
EDUCATION
Carnegie Mellon University
Computational FinanceQuantitative Methods
Pittsburgh, PA
Master of Science in Computational Finance
Aug 2018 - May 2020
Focused on stochastic calculus, derivatives pricing, statistical learning, and high-performance computing for financial applications.
University of Michigan
Computer ScienceApplied Mathematics
Ann Arbor, MI
Bachelor of Science in Computer Science
Sep 2014 - May 2018
Graduated with strong preparation in algorithms, probability, data structures, systems programming, and applied mathematics.
Coco Panda
Quantitative Software Engineer
PROFESSIONAL SUMMARY
Quantitative Software Engineer with 5 years of experience building pricing, risk, and market data systems for trading teams. Strong background in Python, C++, statistical modeling, and low-latency data pipelines. Delivered production tools that improved backtesting speed, model validation quality, and trader decision support.
PROFESSIONAL SKILLS
Quantitative Engineering
PythonC++JavaNumerical MethodsMonte Carlo Simulation
Markets and Modeling
EquitiesOptions PricingFixed IncomeRisk AnalyticsStatistical Modeling
Data and Infrastructure
SQLPandasNumPyPyTorchLinux
PROFESSIONAL EXPERIENCE
Hudson River Trading Analytics
Trading TechnologyCapital Markets
New York, NY
Quantitative Software Engineer
PythonRisk Systems
Jun 2022 - Present
- Built Python and C++ services for intraday risk calculations, reducing portfolio exposure refresh time from 14 minutes to under 4 minutes.
- Partnered with quantitative researchers to productionize factor models, improving signal deployment reliability and cutting release defects by 31%.
- Designed SQL and Pandas validation workflows for market data feeds, identifying stale quote issues before trading impact in 97% of cases.
- Optimized Monte Carlo simulation libraries using multiprocessing and vectorized NumPy routines, increasing scenario throughput by 42% on shared compute nodes.
Apex Quant Solutions
FinTechAlgorithmic Trading
Chicago, IL
Quantitative Developer
C++Backtesting
Jul 2020 - May 2022
- Developed backtesting tools for equities strategies in Python, enabling researchers to evaluate 120,000 daily simulations with reproducible experiment tracking.
- Implemented C++ pricing components for options analytics, lowering batch valuation latency by 35% during end-of-day risk processing.
- Built automated data quality checks for corporate actions and reference data, reducing research dataset exceptions by 28% over two quarters.
- Collaborated with traders and DevOps engineers to deploy Linux-based analytics jobs, improving overnight pipeline completion rate from 89% to 98%.
SELECTED PROJECTS
Derivatives Pricing and Simulation Engine - Quantitative Software Engineer
Nov 2023 - Mar 2024
- Built a Python and C++ Monte Carlo engine for option pricing, reducing simulation runtime by 38% through vectorization and parallel execution.
- Added validation reports comparing model outputs against market quotes, improving exception detection and reducing manual review time by 45%.
Market Microstructure Backtesting Framework - Lead Developer
Apr 2023 - Aug 2023
- Developed an event-driven research pipeline processing five years of tick data, enabling faster strategy iteration across equities and futures datasets.
- Implemented transaction cost modeling and walk-forward testing, improving estimated live performance alignment by 22% during pre-production review.
EDUCATION
Carnegie Mellon University
Computational FinanceQuantitative Methods
Pittsburgh, PA
Master of Science in Computational Finance
Aug 2018 - May 2020
Focused on stochastic calculus, derivatives pricing, statistical learning, and high-performance computing for financial applications.
University of Michigan
Computer ScienceApplied Mathematics
Ann Arbor, MI
Bachelor of Science in Computer Science
Sep 2014 - May 2018
Graduated with strong preparation in algorithms, probability, data structures, systems programming, and applied mathematics.
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