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Quantitative Analyst Jobs in Texas (NOW HIRING)

Design and improve analytical frameworks for VaR, Expected Shortfall, stress testing, backtesting ... Strengthen the quantitative underpinnings of the firm's market risk framework, including model ...

Design and improve analytical frameworks for VaR, Expected Shortfall, stress testing, backtesting ... Strengthen the quantitative underpinnings of the firm's market risk framework, including model ...

Also develops and documents the quantitative tools used to quantify credit risk, provide early ... Performs ad hoc analyses as requested by management. * Leads the implementation planning and ...

Also develops and documents the quantitative tools used to quantify credit risk, provide early ... Performs ad hoc analyses as requested by management. * Leads the implementation planning and ...

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Quantitative Analyst information

See Texas salary details

$52.6K

$124.7K

$223.6K

How much do quantitative analyst jobs pay per year?

As of Aug 10, 2026, the average yearly pay for quantitative analyst in Texas is $124,727.00, according to ZipRecruiter salary data. Most workers in this role earn between $103,900.00 and $135,600.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a quantitative analyst, and why are they important?

To thrive as a Quantitative Analyst, you need a strong background in mathematics, statistics, computer science, and finance, often supported by an advanced degree such as a master's or PhD. Expertise in programming languages like Python, R, or MATLAB, as well as familiarity with financial modeling tools and statistical software, is typically required. Analytical thinking, problem-solving abilities, and clear communication skills help you interpret complex data and convey insights to stakeholders. These competencies are crucial for developing accurate financial models, managing risk, and enabling data-driven decision-making in competitive financial environments.

What does a quantitative analyst do?

The responsibilities of quantitative analysts, or quants, include using mathematical models and statistics to analyze data to assess risks and develop solutions for business issues. In this role, you can work in a variety of industries, from production to finance to insurance. You typically gather and interpret data to help an organization implement a solution for maintaining its fiscal health. Duties vary with the industry. Some positions focus on collecting information from the general public or consumers of particular products through the use of polls and surveys to improve their design and marketing. Other quants work alongside researchers in the health care field to test treatments and medical equipment design.

How does a quantitative analyst typically collaborate with other departments within a financial organization?

Quantitative Analysts frequently work closely with traders, portfolio managers, risk managers, and IT professionals to develop, test, and implement financial models. Effective communication is essential, as they must translate complex quantitative findings into actionable insights for decision-makers. It's common to participate in cross-functional meetings, provide model validation support, and help interpret results for non-technical stakeholders. This collaborative environment fosters both technical skill development and a deeper understanding of the business, which can open doors to broader career opportunities.

What is a quantitative analyst?

Quantitative Analysts, often called 'quants,' are professionals who use mathematical models, statistics, and computer programming to analyze financial data and support decision-making in finance. They develop and implement complex models to assess risk, value financial securities, and identify profitable investment opportunities. Quants are commonly employed by investment banks, hedge funds, asset management companies, and other financial institutions. Their work helps optimize trading strategies, manage risk, and improve financial performance.

What is the difference between Quantitative Analyst vs Data Scientist?

AspectQuantitative AnalystData Scientist
Required CredentialsDegree in finance, mathematics, or statistics; often certifications like CFADegree in computer science, statistics, or related fields; certifications like CAP or data science certifications
Work EnvironmentFinancial firms, investment banks, hedge fundsTech companies, finance, healthcare, and various industries
Employer & Industry UsagePrimarily in finance and investment sectorsAcross multiple industries including tech, healthcare, and retail
Common Search & Comparison IntentUnderstanding roles in finance and investment analysisExploring data analysis and machine learning roles

While both roles involve data analysis and statistical skills, Quantitative Analysts focus on financial modeling and investment strategies within finance firms. Data Scientists have a broader scope, applying data analysis across various industries, often with programming and machine learning expertise.

What are the most commonly searched types of Quantitative Analyst jobs in Texas? The most popular types of Quantitative Analyst jobs in Texas are:
What cities in Texas are hiring for Quantitative Analyst jobs? Cities in Texas with the most Quantitative Analyst job openings:
What are popular job titles related to Quantitative Analyst jobs in TX? For Quantitative Analyst jobs in TX, the most frequently searched job titles are:
Infographic showing various Quantitative Analyst job openings in Texas as of August 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 100% In-person job distribution, with an average salary of $124,727 per year, or $60 per hour.

Quant Analyst - Commodities (Oil)

Verition Group LLC

Houston, TX โ€ข On-site

Full-time

Re-posted 4 days ago


Job description

Verition Fund Management LLC ("Verition") is a multi-strategy, multi-manager hedge fund founded in 2008. ย Verition focuses on global investment strategies including Global Credit, Global Convertible, Volatility & Capital Structure Arbitrage, Event-Driven Investing, Equity Long/Short & Capital Markets Trading, and Global Quantitative Trading.

We are seeking a quantitative researcher to join a world class commodities trading team. This role is focused on building and enhancing data, analytics, and quantitative tools that support discretionary trading decisions. The ideal candidate combines strong coding and statistical foundations with practical experience applying machine learning and early-stage AI techniques to real-world problems. Prior exposure to crude oil markets is strongly preferred.

Responsibilities:

  • Develop and maintain Python-based research, analytics, and data pipelines to support trading and market analysis.
  • Design and manage databases and structured data workflows, including SQL-based querying and cloud-hosted data solutions.
  • Build dashboards and interactive tools (e.g., Streamlit) to visualize market data, signals, and risk metrics for the trading desk.
  • Apply statistical techniques and machine learning methods to analyze historical and real-time market data.
  • Contribute to the development and refinement of quantitative signals and core strategies used in commodities trading.
  • Explore and implement practical AI applications, including NLP and neural network-based approaches, where relevant to the trading process.
  • Work closely with the trader to prioritize projects, translate trading intuition into quantitative frameworks, and iterate quickly.

Qualifications:

  • Python (minimum 3+ years of professional experience; required) for data analysis.
  • Prior experience in commodities markets, particularly oil, is strongly preferred.
  • Git / version control (required).
  • Solid applied statistics (e.g., linear and logistic regression, autocorrelation, time-series concepts).
  • Machine learning fundamentals and common tools (e.g., SVMs, model evaluation best practices).
  • SQL and relational databases (e.g., Snowflake).
  • Dashboarding and data visualization (ideally Streamlit).
  • Cloud platforms (AWS, Azure, or similar).
  • Experience working with large, noisy, real-world datasets.
  • Strong conceptual understanding of NLP and neural networks.
  • Motivated to build tools and research that directly impact P&L.