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PEAK6 · 1 day ago

Quantitative Developer

Austin, TX

Full-time

Hybrid

Entry, Mid Level

$116K/yr - $145K/yr

2+ years exp

PEAK6 Capital Management is a proprietary options trading firm headquartered in Chicago. As a Quantitative Developer, you will collaborate with traders to enhance derivatives pricing models and contribute to research initiatives that translate trader intuition into quantitative frameworks.

Financial ServicesInformation TechnologyTrading PlatformVenture Capital

Responsibilities

Interfacing directly with traders to understand their use cases and nuance of a given model

Working as part of a team on end to end research initiatives to map elements of trader intuition into quantitative models

Optimizing and supporting existing models

Driving the company forward with new ideas that will impact the way we leverage market data

Working with software engineers to ensure the reliable delivery of data and to translating research results to production

Performing any or all of the above responsibilities for, or in support of, PEAK6 HC Holdings LLC or any of its affiliates, including, but not limited to, PEAK6 Group LLC

Qualification

Equity DerivativesPythonQuantitative ResearchMachine LearningDeep LearningStatisticsFinancial EngineeringModel ImplementationData InvestigationCollaboration

Required

Experience working with Equity Derivatives

2-3 years of experience working in either quantitative research, machine learning, deep learning, stats, quantitative trading, or any other role building models on large datasets

Programming experience, Python preferred, particularly around exploring large datasets

Have completed a Bachelors, Masters or PhD in Mathematics, Statistics, Physics, Financial Engineering, or other related quantitative field

A demonstrated track record of applying modeling techniques to real-world problems

The ability to collaborate with subject matter experts in defining appropriate features and metrics for a given model

Comfort implementing models from papers

Experience working with large, noisy, or incomplete data sets and investigating underlying data issues