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Sports Analytics Jobs (NOW HIRING)

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Sports Analytics information

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$64.5K

$125.3K

$179K

How much do sports analytics jobs pay per year?

As of Jul 22, 2026, the average yearly pay for sports analytics in the United States is $125,326.00, according to ZipRecruiter salary data. Most workers in this role earn between $100,000.00 and $149,000.00 per year, depending on experience, location, and employer.

How does a sports analytics professional typically collaborate with coaches and athletes to influence game strategies?

Sports analytics professionals work closely with coaches and athletes by providing data-driven insights that inform tactical decisions and player development. They often translate complex statistical findings into actionable recommendations, such as identifying strengths and weaknesses or optimizing lineups. Effective collaboration requires strong communication skills and the ability to tailor analyses to the team's goals, ensuring that data supports real-time decision-making and long-term strategy. Regular meetings, presentations, and feedback sessions are common to ensure alignment between analytics staff and on-field personnel.

What Is Sports Analytics?

Sports analytics is a field of applied statistics that uses past performance data to provide a competitive advantage to a team or individual player. By using data, an analyst can make suggestions on strategies for a given game, monitor the performance of a player, and provide quick analysis of potentially record-setting activities. Sports analytics focuses on two areas: on-field and off-field. The on-field analysis helps improve the tactics and fitness of players, while the off-field analysis focuses on the business and merchandising aspect of sports. Sports analytics is also used in sports gambling to help casinos and similar companies decide on which odds to offer to customers.

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

To thrive as a Sports Analyst, you need strong statistical analysis skills, a solid understanding of sports rules and strategies, and often a degree in statistics, data science, or a related field. Familiarity with analytics software such as R, Python, SQL, and sports-specific databases is typically expected. Attention to detail, critical thinking, and effective communication help interpret complex data and present actionable insights to coaches and teams. These capabilities are vital for making data-driven decisions that improve team performance and competitive edge.

What is sports analytics?

Sports analytics refers to the use of data and statistical methods to analyze athletic performance, strategies, and business operations within the sports industry. Professionals in this field collect and interpret data to help teams make informed decisions about player recruitment, game tactics, injury prevention, and fan engagement. The insights gained from sports analytics can provide a competitive edge and drive improvements both on and off the field. With advances in technology, the scope of sports analytics has expanded to include machine learning, video analysis, and wearable devices.

What is the difference between Sports Analytics vs Sports Data Analyst?

AspectSports AnalyticsSports Data Analyst
CredentialsDegree in statistics, data science, sports managementDegree in statistics, data science, sports management
Work EnvironmentResearch, modeling, strategic planning in sports organizationsData collection, analysis, reporting for teams and organizations
Industry UsageUsed for performance optimization, game strategy, player evaluationUsed for data reporting, insights, and performance tracking

Sports Analytics and Sports Data Analysts share similar educational backgrounds and work environments, focusing on data-driven decision making in sports. While Sports Analytics often involves developing models and strategic insights, Sports Data Analysts primarily focus on collecting and reporting data. Both roles are essential in sports organizations, but Sports Analytics tends to have a broader scope in strategic planning and predictive modeling.

What cities are hiring for Sports Analytics jobs? Cities with the most Sports Analytics job openings:
What are the most commonly searched types of Sports Analytics jobs? The most popular types of Sports Analytics jobs are:
What states have the most Sports Analytics jobs? States with the most job openings for Sports Analytics jobs include:
Infographic showing various Sports Analytics job openings in the United States as of July 2026, with employment types broken down into 95% Full Time, 3% Part Time, and 2% Contract. Highlights an 80% Physical, 6% Hybrid, and 14% Remote job distribution, with an average salary of $125,326 per year, or $60.3 per hour.
Senior Quantitative Researcher - Risk Modeling

Senior Quantitative Researcher - Risk Modeling

Swish Analytics

San Francisco, CA • On-site

Full-time

Posted 8 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.