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Quantitative Researcher (Experienced - Ph.D.)

$175k - $250k

Old Mission Capital

Quantitative Researcher (Experienced - Ph.D.)

Old Mission is a global proprietary trading firm that leverages state-of-the-art technology and research to identify and execute profitable trading strategies across multiple asset classes around the world. Our offices in Chicago, New York, and London are all composed of naturally-curious individuals who thrive in a team environment and constantly strive for improvement.

About the Position

We are actively seeking a Quantitative Researcher (Ph.D.) to join our Chicago office. In this role, you will collaborate directly with our options trading group, contributing to the enhancement of existing pricing models, and spearheading the development of cutting-edge models and tools. The primary focus will be on efficiently pricing volatility-based derivatives across diverse asset classes. As a key member of the team, you will actively participate in the options research idea generation process and may also take the lead in expanding the capabilities of the quant team to further enhance the options desk.

Responsibilities

  • Conceptualize and implement cutting-edge derivative pricing models for single-asset derivatives, as well as pricing models for widely traded equity volatility products such as variance swaps, volatility swaps, and VIX options.
  • Evaluate existing options pricing models, scrutinizing aspects such as calibration accuracy and Greeks stability to identify any anomalies. Investigate and propose enhancements to address identified issues.
  • Develop more resilient and stable alternative options pricing models, including exploring and suggesting new parameterizations for the volatility surface and designing improved filtering rules for market data quotes during calibration.
  • Develop options specific research infrastructure and libraries in Python.
  • Analyze market data and microstructure to identify patterns, facilitating the exploration of trading ideas.
  • Actively participate in the idea generation process for options research, contributing insights and innovations.
  • Collaborate closely with traders, engaging in the analysis of data and jointly developing new tools and ideas to identify patterns in the market.

Required Skills

  • Must have a Masters or Ph.D. in a quantitative discipline, encompassing fields such as computer science, engineering, physics, mathematics, statistics, or other hard sciences.
  • Proficient in derivatives and volatility modeling, including the construction and calibration of forward curves and volatility surfaces. Possess extensive knowledge of equity and index derivative products, including variance derivatives, VIX, and other related instruments. Experience with dividend swaps and futures is considered advantageous.
  • Must have programming skills in Python and C++ are a prerequisite.
  • Demonstrated exceptional written and verbal communication skills, with the ability to manage multiple tasks in a time-sensitive, collaborative, and fast-paced environment.
  • A team-oriented problem solver with the capacity to thrive under pressure, exhibiting a genuine passion for the financial markets.
  • Highly organized, detail-oriented, and adept at managing multiple work streams concurrently.
  • Proven track record of trustworthiness and performance, consistently adhering to the highest ethical standards.

Benefits and Perks

  • Fully paid Medical, Dental, Vision, Disability, and Life Insurance
  • Fully stocked kitchen; free breakfast and lunch every day on-site
  • Tuition Reimbursement Program
  • 401(k) with employer match
  • Paid Vacation, Sick, and Parental leaves
  • Commuter and Flexible Spending Programs

Base Salary Range

$175,000 - $250,000 - Salaries are based on numerous factors such as skills, experience, and education. Our compensation package also includes a discretionary bonus and a comprehensive benefits program for full-time employees. For more information, reach out to your recruiter.

Old Mission Capital
Vacancy posted 4 days ago
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