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Quantitative Finance Jobs (NOW HIRING)

Quantitative Researcher

$150K - $200K/yr

Exploring new methodologies and approaches to stay at the forefront of quantitative finance. Staying informed about market trends, emerging technologies, and advancements in quantitative finance.

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Quantitative Finance information

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$31K

$90.6K

$146K

How much do quantitative finance jobs pay per year?

As of Jun 12, 2026, the average yearly pay for quantitative finance in the United States is $90,579.00, according to ZipRecruiter salary data. Most workers in this role earn between $35,000.00 and $119,000.00 per year, depending on experience, location, and employer.

Do quants make a lot of money?

Quantitative finance professionals, or quants, often earn high salaries due to their specialized skills in mathematics, programming, and financial modeling. Compensation typically includes base salary, bonuses, and profit sharing, with total earnings frequently exceeding those of many other finance roles, especially at senior levels or in hedge funds and investment banks.

What is quantitative finance?

Quantitative finance is a field that uses mathematical models, statistics, and computational techniques to analyze financial markets and securities. Professionals in this area, known as 'quants,' develop algorithms and models to price assets, manage risk, and optimize investment strategies. Quantitative finance plays a critical role in investment banks, hedge funds, asset management firms, and financial technology companies. The field requires strong skills in mathematics, programming, and finance.

What is a quantitative finance career?

A quantitative finance career involves using mathematical models, statistical techniques, and programming skills to analyze financial markets and develop trading strategies, risk management tools, or investment models. Professionals in this field often work with large datasets, employ tools like Python or R, and may hold advanced degrees in mathematics, finance, or related fields.

What are the key skills and qualifications needed to thrive in Quantitative Finance, and why are they important?

To thrive in Quantitative Finance, you need strong mathematical, statistical, and analytical skills, typically supported by an advanced degree in mathematics, finance, engineering, or a related field. Proficiency with programming languages like Python, R, or C++, as well as experience with financial modeling and platforms such as MATLAB or Bloomberg Terminal, is highly valued. Exceptional problem-solving abilities, attention to detail, and effective communication are crucial soft skills for collaborating on complex financial projects. These skills enable professionals to develop and implement sophisticated models that drive informed investment decisions and risk management in high-stakes financial environments.

What jobs can you get with quantitative finance?

With a background in quantitative finance, common roles include quantitative analyst, risk manager, financial engineer, and algorithmic trader. These positions typically require strong skills in mathematics, programming, and data analysis, often using tools like Python, R, or MATLAB, and may require relevant certifications such as CFA or FRM.

What is the difference between Quantitative Finance vs Quantitative Analysis?

AspectQuantitative FinanceQuantitative Analysis
Required CredentialsDegree in Finance, Mathematics, or related fields; often CFA or FRM certificationsDegree in Mathematics, Statistics, or Finance; certifications like CFA are common
Work EnvironmentFinancial institutions, hedge funds, investment banksAsset management firms, banks, trading desks
Employer & Industry UsageFocuses on developing trading strategies, risk management, and financial modelingAnalyzes data to inform trading decisions, risk assessment, and investment strategies

Quantitative Finance and Quantitative Analysis share overlapping skills and credentials, but Quantitative Finance emphasizes developing financial models and trading strategies, while Quantitative Analysis focuses on data analysis to support investment decisions. Both roles are vital in finance but serve different primary functions.

What are some common challenges faced by professionals in quantitative finance roles, and how can they be addressed?

Professionals in quantitative finance often encounter challenges such as managing large and complex data sets, staying updated with rapidly evolving financial models, and ensuring accurate risk assessment in volatile markets. Collaboration with technology and trading teams is crucial to develop robust algorithms and implement models effectively. Continuous learning and adaptability are key, as the field demands keeping pace with new programming languages, statistical methods, and regulatory changes.

Is 30 too late to become a quant?

Quantitative finance is accessible to individuals who develop strong skills in mathematics, programming, and finance, regardless of age. Many quants transition into the field after gaining relevant experience or education, and age is less of a barrier than technical proficiency and continuous learning.
More about Quantitative Finance jobs
What cities are hiring for Quantitative Finance jobs? Cities with the most Quantitative Finance job openings:
What are the most commonly searched types of Quantitative Finance jobs? The most popular types of Quantitative Finance jobs are:
What states have the most Quantitative Finance jobs? States with the most job openings for Quantitative Finance jobs include:
Infographic showing various Quantitative Finance job openings in the United States as of June 2026, with employment types broken down into 95% Full Time, 3% Part Time, and 2% Contract. Highlights an 81% Physical, 8% Hybrid, and 11% Remote job distribution, with an average salary of $90,579 per year, or $43.5 per hour.

Quantitative Finance Analyst

Bank of America

Charlotte, NC

Full-time

PTO

Posted 18 days ago


Job description

Job Description:

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day.
Being a Great Place to Work and providing a culture of caring is core to how we drive Responsible Growth. We are intentional about fostering an inclusive workplace where every teammate has the opportunity to succeed, build a career and contribute to our shared success. This includes attracting and developing exceptional talent, recognizing and rewarding performance, and supporting our teammates' physical, emotional, and financial wellness through affordable, competitive and flexible benefits.
We value the unique perspectives individuals bring from all backgrounds and career paths - whether shaped by military service, community college education, or a wide range of work and life experiences. These journeys foster resilience, leadership and innovation, strengthening our workforce and positively impact the communities we serve.
Bank of America is committed to an in-office culture that supports collaboration, engagement, and career development. Our approach includes clear in-office expectations, while providing an appropriate level of flexibility based on role-specific responsibilities and business needs.
At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!

Job Description:
This job is responsible for conducting quantitative analytics and modeling projects for specific business units or risk types. Key responsibilities include developing new models, analytic processes, or systems approaches, creating technical documentation for related activities, and working with Technology staff in the design of systems to run models developed. Job expectations include having a broad knowledge of financial markets and products.

Responsibilities:

  • Performs end-to-end market risk stress testing including scenario design, scenario implementation, results consolidation, internal and external reporting, and analyzes stress scenario results to better understand key drivers

  • Supports the planning related to setting quantitative work priorities in line with the bank's overall strategy and prioritization

  • Identifies continuous improvements through reviews of approval decisions on relevant model development or model validation tasks, critical feedback on technical documentation, and effective challenges on model development/validation

  • Supports model development and model risk management in respective focus areas to support business requirements and the enterprise's risk appetite

  • Supports the methodological, analytical, and technical guidance to effectively challenge and influence the strategic direction and tactical approaches of development/validation projects and identify areas of potential risk

  • Works closely with model stakeholders and senior management with regard to communication of submission and validation outcomes

  • Performs statistical analysis on large datasets and interprets results using both qualitative and quantitative approaches

Global Risk Management (GRM) leads bank-wide initiatives for management of all aspects of risk, including strategic, market, credit, compliance, liquidity, operational, model and reputational risk matters to support sustainable, profitable corporate growth.
In a data driven economy, strategic data asset management is foundational to add to the enterprise value. Within GRM, we have established the Data Strategy & Management (DSM) function. A key pillar of this function is a strong data management, data architecture and data platforms foundation.

Under the GRM DSM Executive's leadership, the Quantitative Finance Analyst will help design features to simplify and optimize the data environment through data centric AI, and be accountable for contributing to the architecture & prototyping along with core algorithms for various data and AI powered solutions. Additionally, the analyst will also help evaluate data & AI tools and conduct proof of concepts & pilot projects to arrive at recommended solutions and develop remediation plans to implement those solutions.

Responsibilities:

  • Demonstrates knowledge of data & AI solutions, data platforms, context engineering, data management & model governance practices and standards.

  • Uses architecture tools to create design artifacts, including but not limited to Data models/architecture, API design and data solutions architecture.

  • Undertakes architecture & solutioning for data and data centric AI solutions as well as one or more data management products such as Data Catalog, Data Lineage, Data Feeds Registry, Data Quality and related data services.

  • Design data products to provide high quality data for quantitative modeling solutions for GRM.

  • Applies knowledge to perform regular assessments of the health and maturity of data & information capabilities for the Global Risk Management (GRM) domain.

  • Evangelize and design new data & AI solutions and capabilities to support risk lines of businesses.

  • Participates in efforts to define the mission, goals, critical success factors, principles, and procedures for data strategy and information architecture.

  • Understands the end-to-end change impact by managing linkages from information capabilities to technical assets (operational + analytical)

  • Maintains integrated logical data models and data flows to understand data and its interdependencies regardless of its usage pattern.

Required Qualifications:

  • Bachelor's degree in computer science / engineering, Data Science or Analytics and 4+ years of experience in data & AI platform/solutions and data management; or if Master's degree, 2+ years' experience.

  • Strong experience working with risk reporting systems, data warehouses, reporting tools, and governance frameworks.

  • Familiarity with data quality frameworks, metadata management, data lineage tools, and control monitoring.

  • Working knowledge of AI and GenAI patterns, lang graph, lang chain, embedding, chunking, RAG, vector stores as well as graphical context processing.

  • Proven track record of defining and delivering product roadmaps for complex data management or reporting platforms.

  • Strong stakeholder management and cross-functional leadership skills.

  • Proficiency in Agile delivery methodologies (e.g. Scrum, SAFe).

  • Excellent communication skills (written, verbal and presentation) with the ability to translate regulatory language into actionable technical requirements.

  • Strong experience driving the design and development of data & AI solutions, data management & governance products as well as data and reporting platforms.

  • Expertise in architecting complex design patterns, microservices, API design, data warehouse and data lakes and data pipelines.

  • Ability to drive data strategy and deep understanding of industry paradigms such as data mesh, data contracts, integration fabric etc.

  • Experience with relational and NoSQL data stores and big data environments.

  • Hands-on expertise with data technologies and computing frameworks including but not limited to, Python, Spark, Airflow, Javascript and SQL.

  • Ability to research new data technologies, architect novel data solutions for business problems and prove design approach through hands-on prototyping.

  • Experience with data modeling for complex data pipelines, data lake and data platforms.

  • Strong experience with data management platform such as Collibra (preferred) or understanding of open-source frameworks such as Apache Atlas, Amundsen, Datahub, Marquez etc.

  • Exceptional communication skills and the ability to communicate effectively at all levels of the organization; this includes written and verbal communications as well as visualizations.

Desired Qualifications:

  • Working knowledge of data storage layers / formats such as Apache Iceberg, Hudi, Delta Lake etc. as well as Parquet, JSON and Avro.

  • Experience with Graph processing and storage technologies such as Knowledge Graphs / Property Graphs with working knowledge of at-least one graph store such as TigerGraph (preferred).

  • Exposure to Linked Data / Open Data, and GenAI based data solutions is a plus.

Skills:

  • Critical Thinking

  • Quantitative Development

  • Risk Analytics

  • Risk Modeling

  • Technical Documentation

  • Adaptability

  • Collaboration

  • Problem Solving

  • Risk Management

  • Test Engineering

  • Data Modeling

  • Data and Trend Analysis

  • Process Performance Measurement

  • Research

  • Written Communications

Shift:

1st shift (United States of America)

Hours Per Week:

40

Pay Transparency details

US - NJ - Jersey City - 525 Washington Blvd (NJ2525)Pay and benefits informationPay range$89,800.00 - $153,300.00 annualized salary, offers to be determined based on experience, education and skill set.Discretionary incentive eligibleThis role is eligible to participate in the annual discretionary plan. Employees are eligible for an annual discretionary award based on their overall individual performance results and behaviors, the performance and contributions of their line of business and/or group; and the overall success of the Company.BenefitsThis role is currently benefits eligible. We provide industry-leading benefits, access to paid time off, resources and support to our employees so they can make a genuine impact and contribute to the sustainable growth of our business and the communities we serve.