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Phd Quant Jobs in Texas (NOW HIRING)

Citadel

$4.5K - $5.8K/wk

You'll get to challenge the impossible in quantitative research by applying sophisticated and ... PhD degree in mathematics, statistics, physics, computer science, or another highly quantitative ...

Expand Energy

Quantitative Risk Analyst

Spring, TX · On-site

Master's degree or PhD preferred in a quantitative discipline such as Mathematics, Statistics, Physics, Engineering, Computer Science, Econometrics, Finance, Applied Economics or related. Experience

Expand Energy

Quantitative Risk Analyst

Spring, TX · On-site

Master's degree or PhD preferred in a quantitative discipline such as Mathematics, Statistics, Physics, Engineering, Computer Science, Econometrics, Finance, Applied Economics or related. Experience

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Phd Quant information

What is a PhD Quant?

A PhD Quant, short for Quantitative Analyst with a PhD, is a professional who uses advanced mathematical, statistical, and computational techniques to analyze financial markets and develop complex models for trading, risk management, or investment strategies. They typically work in banks, hedge funds, or financial technology firms. PhD Quants leverage their deep expertise in fields like mathematics, physics, computer science, or engineering to solve challenging problems and gain insights that drive financial decision-making. Their work often involves programming, data analysis, and the implementation of quantitative models.

What are the key skills and qualifications needed to thrive as a PhD Quant?

To thrive as a PhD Quant, you need a strong background in mathematics, statistics, and programming, typically supported by a PhD in a quantitative field such as mathematics, physics, finance, or engineering. Expertise in technical tools such as Python, C++, R, and experience with statistical modeling systems and quantitative finance libraries is expected. Analytical thinking, problem-solving abilities, and effective communication are standout soft skills in this role. These skills and qualities are crucial for developing complex models, interpreting data accurately, and collaborating across multidisciplinary teams in high-stakes financial environments.

What are the typical collaboration dynamics for a PhD Quant within a financial institution?

PhD Quants frequently work in close collaboration with traders, risk managers, and software engineers to develop and implement quantitative models for pricing, risk assessment, and trading strategies. While a significant portion of the work involves independent research and model development, regular meetings and cross-functional teamwork are essential to ensure models align with business objectives and regulatory requirements. Effective communication skills are important, as PhD Quants often need to explain complex mathematical concepts to colleagues with varying technical backgrounds.

What is the difference between Phd Quant vs Quant Analyst?

AspectPhd QuantQuant Analyst
Required CredentialsPhD in Mathematics, Statistics, or related fieldBachelor's or Master's degree, often with quantitative skills
Work EnvironmentResearch-focused, often in finance or hedge fundsTrading floors, financial institutions, or asset management firms
Industry UsagePrimarily in hedge funds, investment banks, and proprietary tradingIn asset management, hedge funds, and banks

The main difference between a Phd Quant and a Quant Analyst lies in their educational background and focus. Phd Quants typically hold doctoral degrees and focus on developing complex models and research, while Quant Analysts often have master's or bachelor's degrees and focus on applying models to trading strategies. Both roles are integral to quantitative finance but differ in scope and depth of research.

Do quant firms hire PhDs?

Quant firms frequently hire PhDs, especially in fields like mathematics, physics, computer science, and engineering, to develop and implement complex trading algorithms and models. Candidates typically need strong quantitative skills, programming experience in languages such as Python or C++, and a solid understanding of financial markets. A PhD can provide a competitive edge in securing roles in quantitative research, trading, or risk management within these firms.

How much do PhD quants make?

PhD quants typically earn between $150,000 and $300,000 annually, with compensation increasing based on experience, location, and the complexity of their quantitative models. Many also receive bonuses and profit-sharing, especially in finance and hedge fund environments where advanced statistical and programming skills are essential.

What are popular job titles related to Phd Quant jobs in Texas?

For Phd Quant jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Phd Quant jobs in Texas look for?

The top searched job categories for Phd Quant jobs in Texas are:

What cities in Texas are hiring for Phd Quant jobs?

Cities in Texas with the most Phd Quant job openings:

Phd Quant jobs near you
Infographic showing various Phd Quant job openings in Texas as of August 2026, with employment types broken down into 1% Internship, 87% Full Time, 9% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.

Quantitative Research Intern, PhD (Summer 2027)

Optiver

Austin, TX • On-site

Full-time, Temporary, Internship

Re-posted 7 days ago

Job description

As a Quantitative Research Intern, you'll work alongside researchers, engineers, and traders to tackle some of the most challenging quantitative problems in global financial markets. You'll analyze large-scale datasets, develop predictive models and algorithms, and apply statistical and machine learning techniques to uncover patterns in market behavior. AI-driven research at Optiver is where competitive advantage is built, transforming ideas, models, and insights into trading strategies that operate in live markets.
This opportunity is also available in our Chicago office.
What You'll Do:
Led by our dedicated Education team, you'll build a strong foundation in market structure and options theory. This internship follows an apprentice-style learning model where you'll work alongside an experienced researcher and contribute to a project that's aligned with current business needs. Throughout the internship, you'll gain exposure to the AI tools and technologies that support research and development across the business, with the opportunity to contribute to several key areas:
  • Develop predictive models and machine learning systems to better understand market behavior and identify trading opportunities
  • Analyze large-scale market and order-flow data to uncover signals, evaluate hypotheses, and improve trading performance
  • Build and test statistical and stochastic models for pricing, forecasting, and risk management
  • Apply modern research techniques, including deep learning and AI-enabled workflows, to accelerate discovery and improve research efficiency

What You'll Get:
You'll join a culture of collaboration, continuous improvement, and excellence, surrounded by curious thinkers and creative problem-solvers. Together, you'll tackle some of the toughest challenges in the financial markets by leveraging cutting-edge machine learning research to develop innovative, real-world solutions.
In addition, you'll receive:
  • The opportunity to work alongside best-in-class professionals from over 40 different countries
  • The opportunity to earn a return internship or full-time offer in Chicago, Austin, or New York City based on performance
  • A highly-competitive internship compensation package
  • Optiver-covered flights, living accommodations, and commuting stipends
  • Extensive office perks, including breakfast, lunch, snacks, regular social events, clubs, sporting leagues, and more

What To Expect:
As part of our assessment process, you may be invited to participate in a multi-day, on-site evaluative program. Through hands-on workshops, technical discussions, and direct exposure to our researchers and traders, you'll gain insight into how research is applied at Optiver and how PhD students transition successfully into industry. Attendance and successful completion of this program may be required to receive an internship offer.
Who You Are:
  • Currently enrolled in a PhD program in Statistics, Computer Science, Machine Learning, Mathematics, or a related STEM field with outstanding academic performance
  • Expected graduation between December 2027 - June 2029 and available to intern during Summer 2027
  • Open to full-time opportunities upon graduation in 2028 or 2029
  • Solid foundation in mathematics, probability, and statistics
  • Excellent research, analytical, and modeling skills
  • Experience applying machine learning methods to real-world research problems, such as time-series analysis, prediction, forecasting, pattern recognition, optimization, or decision-making
  • Proficiency in any programming language
  • Strong interest in working in a fast-paced, collaborative environment
  • Fluent in English with strong written and verbal communication skills

Who We Are:
Optiver is a leading technology- and research-driven trading firm. Our teams of scientists, engineers, mathematicians, and traders work side by side to develop, test, and scale ideas that shape how we understand and trade global markets. Powered by a global platform built for rapid experimentation and iteration, we combine the scientific rigor of a research institution with the pace of a technology company.
Our differences are our edge. Optiver does not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, physical or mental disability, or other legally protected characteristics.
Optiver is supportive of US immigration sponsorship for this role.
*Optiver has a global application re-apply policy for our intern and graduate roles. If you have completed an online assessment or interviewed for a quantitative graduate or internship role at any Optiver location in the past 8 months, please note that you are not yet eligible to reapply. We welcome you to re-apply to after the 8-month cool off period.

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