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Data Scientist III - FCRM Risk Modeling

Charlotte, NC · On-site

$96K - $155K/yr

The US Financial Crime Risk Modeling & Advanced Analytics team within US Financial Crime department is responsible for developing, maintaining, and enhancing the Enterprise Anti-Money Laundering ...

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How much do risk modeling jobs pay per hour?

As of Aug 10, 2026, the average hourly pay for risk modeling in the United States is $30.34, according to ZipRecruiter salary data. Most workers in this role earn between $19.47 and $38.70 per hour, depending on experience, location, and employer.

What does a risk modeling do?

A risk modeler analyzes data to identify and quantify potential risks that could impact an organization or project. They develop mathematical models using statistical tools and software to assess risk levels, helping companies make informed decisions and manage uncertainties effectively.

What is the difference between Risk Modeling vs Risk Analyst?

AspectRisk Modeling
AspectRisk Modeling

Risk Modeling involves developing quantitative models to predict and assess potential risks using statistical and mathematical techniques. Risk Analysts interpret these models, analyze data, and provide insights to support decision-making. While Risk Modeling focuses on creating models, Risk Analysts apply these models to real-world scenarios. Both roles often require similar credentials like certifications in risk management and work in similar environments such as finance, insurance, or banking. Understanding the distinction helps organizations allocate resources effectively and professionals target their skill development.

What is risk modeling?

Risk modeling is the process of using statistical and mathematical techniques to predict potential risks and their impact on an organization or financial system. Professionals in this field develop models to assess the likelihood and severity of various risks, such as credit, market, operational, or environmental risks. These models help organizations make informed decisions, comply with regulations, and minimize potential losses by preparing for uncertain events.

Do risk analysts make good money?

Risk analysts typically earn a competitive salary that varies by industry, experience, and location. Entry-level positions often start around $60,000 annually, with experienced professionals earning over $100,000, especially if they hold certifications like FRM or CFA. The role often involves analytical skills, proficiency with data tools, and understanding of financial or operational risks.

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

To thrive as a Risk Modeler, you need strong quantitative analysis skills, a background in statistics or mathematics, and typically a relevant degree such as finance, economics, or engineering. Familiarity with statistical software (like SAS, R, or Python), risk management frameworks, and regulatory requirements is essential, and certifications such as FRM or CFA are often valued. Attention to detail, problem-solving abilities, and effective communication help translate complex data into actionable insights for various stakeholders. These skills ensure accurate risk assessment, regulatory compliance, and informed decision-making in high-stakes environments.

What are some common challenges faced by professionals in risk modeling roles, and how are they typically addressed?

Risk modeling professionals often encounter challenges such as managing incomplete or inconsistent data, keeping up with rapidly evolving regulatory requirements, and ensuring their models remain accurate as market conditions change. To address these, teams frequently collaborate with data engineers, compliance specialists, and business stakeholders to validate data sources, implement robust model governance processes, and regularly update models. Continuous learning and cross-functional communication are key to staying effective in this dynamic environment.
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Risk Modeling jobs near you
Infographic showing various Risk Modeling job openings in the United States as of August 2026, with employment types broken down into 100% Full Time. Highlights an 89% In-person, and 11% Remote job distribution, with an average salary of $63,100 per year, or $30.3 per hour.

Senior Quantitative Researcher - Risk Modeling

Swish Analytics

San Francisco, CA • On-site, Remote

Full-time

Re-posted 27 days ago

Job description

Company Description
Swish Analytics is a sports analytics and trading company building the next generation of predictive sports analytics and exchange-based trading products. We believe that profitable trading is a challenge rooted in engineering, mathematics, and market expertise-not intuition. We're seeking team-oriented individuals with an authentic passion for quantitative trading who can execute in a fast-paced environment without sacrificing technical excellence.
As we expand our presence on betting exchanges, we're building infrastructure and strategies akin to those found in traditional financial markets. Our challenges are unique, and we hope you're comfortable in uncharted territory.
Role Overview
As a Senior Quantitative Researcher, you will own end-to-end research and production pipelines for one or more trading strategies. You'll lead research initiatives that generate alpha and improve execution quality, mentor junior researchers, and collaborate closely with our Trading desk to translate quantitative insights into profitable systematic strategies while maintaining rigorous risk management.
Core Responsibilities
  • Own end-to-end research and production pipelines for a strategy
  • Lead alpha research initiatives leveraging advanced statistical and machine learning techniques
  • Process and analyze high-frequency tick data, order book snapshots, and market microstructure signals with sub-millisecond latency requirements
  • Analyze price formation, market liquidity dynamics, and limit order book imbalances across electronic venues
  • Build and run Monte Carlo simulations to estimate P&L distributions, risk exposures, and portfolio dynamics
  • Develop, backtest, and optimize quantitative trading strategies with rigorous statistical validation
  • Interpret complex model outputs and communicate alpha generation mechanisms to portfolio managers
  • Write modular, clean, and efficient Python code; build custom analytics libraries and research frameworks
  • Lead design reviews and establish data quality and research reproducibility standards
  • Guide 1-2 junior researchers through project delivery and model development
  • Proactively engage with traders and infrastructure teams to clarify research objectives and resolve data dependencies

Risk Modeling
  • Design and maintain real-time risk monitoring systems across multi-asset portfolios
  • Build models for dynamic position sizing, portfolio optimization, and factor exposure management
  • Develop stress testing and scenario analysis frameworks for tail-risk events and regime changes
  • Collaborate with Trading and Risk Management to define VaR limits, leverage constraints, and implement automated risk controls

Requirements
  • Minimum of 5 years of experience in quantitative research, systematic trading, or statistical modeling
  • Master's degree in a quantitative discipline (Mathematics, Statistics, Physics, Computer Science, Financial Engineering) strongly preferred; PhD a plus
  • Expert-level Python skills; able to build production-grade research and trading systems
  • Strong SQL skills; experience with complex queries on tick databases and time-series datasets
  • Deep experience with Monte Carlo methods, stochastic calculus, and probabilistic modeling
  • Proven ability to develop, backtest, and deploy systematic trading strategies with demonstrable P&L
  • Experience processing high-frequency tick data and real-time market feeds
  • Familiarity with AWS or similar cloud infrastructure for large-scale backtesting and research
  • Track record of mentoring junior quantitative researchers
  • Excellent communication skills; ability to present complex quantitative research to portfolio managers and trading desks
  • Experience designing enterprise-grade risk management systems with real-time Greeks calculation
  • Strong understanding of factor models, correlation structure, concentration risk, and portfolio attribution

Nice to Have
  • Proficiency in Rust, C++, or other systems languages for performance-critical components
  • Experience with MLOps, model monitoring, and adaptive retraining pipelines for regime detection
  • Background in derivatives pricing, options market making, or volatility arbitrage
  • Familiarity with FIX protocol, Betfair or Matchbook API experience, and ultra-low-latency trading infrastructure

Swish Analytics is an Equal Opportunity Employer. All candidates who meet the qualifications will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, pregnancy status, genetic, military, veteran status, marital status, or any other characteristic protected by law. The position responsibilities are not limited to the responsibilities outlined above and are subject to change. At the employer's discretion, this position may require successful completion of background and reference checks. Base salary is one hundred and fifty to two hundred and fifty thousand (plus bonus), depending on experience.
Department Trading Analytics Role Trading Data Science Locations San Francisco, CA - Remote Remote status Fully Remote

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