Lead Quantitative Analyst

About the Role

Key Responsibilities

• Analyze existing implementations of option pricing models and conduct empirical research into their efficacy across different geographies and liquidity levels.
• Apply statistical methods including classification models, regressions, and machine learning to identify features of derivatives suited for each model.
• Collaborate with engineering teams to ensure effective and consistent quality in analytic pipelines.
• Apply machine learning techniques to detect patterns and anomalies in applying Model Fitted Theoretical pricing to various options categories.
• Utilize statistical modeling, regression analysis, and classification methods to improve calibration of Model Fitting methods.
• Implement feature engineering approaches to enhance real-time Model Fitting applications using approaches such as SABR, Mixture Models, or SVI.
• Collaborate with hedge funds, asset managers, and proprietary trading firms to understand their options analytics needs and use cases.
• Assist sales and marketing teams in positioning and selling analytic data services to institutional clients.

Requirements

• Master's or PhD degree in Quantitative Finance, Computer Science, Data Science, Statistics, Engineering, or a related field.
• 6 years of experience with a Master's degree or 3 years with a PhD in a quantitative, data science, or research role at a financial institution such as a bank, fund manager, market maker, or risk analytics vendor.
• Strong programming skills in Python (pandas, NumPy, scikit-learn) and SQL; experience with R is a plus.
• Experience with Java; strong Java skills are a plus.
• Experience with time-series analysis and scalable computing.
• Familiarity with machine learning techniques (supervised/unsupervised learning, feature selection, anomaly detection) including regression and decision tree methods like Random Forest.
• Knowledge of quantitative finance models and risk modeling.
• Experience working with historical datasets for trading, backtesting, and risk management.
• Familiarity with options and futures market structure, volatility analysis, and pricing models.
• Strong ability to translate quantitative findings into actionable production engineering results.
• Ability to work effectively with sales, marketing, and business development teams.

Nice to Have

• Ability to apply machine learning in finance, with relevant use cases.

Qualifications

• Educational background as specified in the requirements (Master's or PhD in relevant fields).

Benefits & Perks

• Fair and competitive salary and incentive compensation packages with an upside for overachievement.
• Generous paid time off, including vacation, personal days, sick days, and community service days.
• Flexible, hybrid work environment.
• Health, dental, and vision benefits, including telemedicine and mental health services.
• 2:1 401(k) match, up to 8% immediately upon hire.
• Discounted Employee Stock Purchase Plan.
• Tax Savings Accounts for health, dependent, and transportation.
• Employee referral bonus program.
• Volunteer opportunities.
• Complimentary lunch, snacks, and coffee in offices.
• Paid Tuition assistance and education opportunities.
• Charitable giving company match.
• Paid parental leave and fertility benefits.
• On-site gyms and discounts to fitness centers.