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Valkyrie Trading

Quantitative Researcher (Experienced)

Chicago, Illinois, United States

Location
Chicago, Illinois, United States
Work
On-site
Employment
Full-time
Level
Mid level
Function
Data & AI
Pay
$155K – $190K a year (read from the ad)
Posted
47 months ago

Pay and the market here

The top of this role’s range, $190K a year, is 6% above the median of $180K for mid level data jobs in Chicago (middle half $140K – $225K, 36 jobs that state pay).

265 data jobs open in Chicago: 23% remote, 66 posted in the last 30 days.

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About the role

What You’ll Do

At Valkyrie, Quantitative Researchers are expected to apply their quantitative skill set to solve challenging problems in financial modeling, algorithm development, and system optimizations. In this role, you will have an opportunity to work with traders and software developers in a highly collaborative environment to improve Valkyrie’s complex, ever-evolving algorithmic infrastructure. Specific responsibilities can range from building tools for traders to exploring new trading ideas, to writing algorithms from scratch, to automating and refining existing strategies, to analyzing and optimizing system performance. You will also inform traders about the market developments, tools, and models that drive Valkyrie’s trading activities.

Quantitative Researchers here focus on one of two areas: modeling or execution. Modeling Quantitative Researchers are expected to design automated market-making and risk management algorithms, and to build statistically driven position taking strategies. Execution Quantitative Researchers are expected to perform post-trade analysis, system behavior investigations, algo parameter optimizations, market microstructure studies, and other execution system optimizations.

This role is for experienced applicants. Those with 3+ years of relevant experience will be considered for a Senior-level role.

What You’ll Need

  • 2+ years of full-time, professional experience in quantitative research, trading, or technology role—ideally in an algorithmic trading firm or a similar high-performance environment
  • Degree in mathematics, physics, engineering, computer science or equivalent
  • Ability to write and maintain a repository of clean, organized, object-oriented Python code; C++ experience is a plus
  • Proven experience in Options and volatility modeling, or statistical analysis on large data sets OR Diagnosing and optimizing algo logic in latency-sensitive or high-throughput distributed systems
  • Expertise working with MySQL or similar databases in conducting historical research and back-testing
  • Passion for innovation and solving open-ended, data-rich problems
  • Strong communication skills, both written and verbal, with the ability to explain complex, technical ideas clearly
  • Collaborative attitude with a competitive drive and comfort with metrics-based performance evaluation
  • Practical mindset with a focus on delivering impactful, production-grade solutions

What You’ll Get

  • Competitive salaries and performance bonuses
  • Top-tier medical and dental coverage
  • Relocation assistance packages
  • Generous vacation policy
  • Training opportunities and continuing education
  • Catered lunch, snacks, and beverages
  • Group outings and company parties
  • Casual dress environment

Salary range

  • $155,000 - $190,000 a year
  • This position is also eligible for an annual discretionary bonus.

Our Vision

  • Valkyrie’s vision is to be an organization where curious, driven, and innovative people are able to compete and win in the world’s most competitive landscape – the financial markets. Daily competition in the financial markets provides the ideal arena to push ourselves further as we are ever striving to grow, improve, and win. We recognize that there are extraordinary opportunities to be had every day, and by exploring, collaborating, and building, we can accomplish remarkable results together.

Source

Posted by Valkyrie Trading on their own hiring page, jobs.lever.co. OptiTalent shows it as published, read 07 Nov 2026 12:10 UTC; the original is the reference.

View the original ad ↗

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