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Quantitative Sports Trading Jobs (NOW HIRING)

AEG

Sports Trading Analyst

San Francisco, CA · On-site

$70K - $120K/yr

This role is suited to someone with strong quantitative reasoning, excellent decision-making under pressure, and a deep interest in how markets are formed, odds move, and how to engineer accurate ...

AEG

Trading Analyst

San Francisco, CA · On-site

$70K - $110K/yr

... trading operation ... In this role, you will work at the intersection of sports intelligence, quantitative modelling ...

Dime Line Trading

Quantitative Trader

Chicago, IL

Take ownership of operational and production workflows supporting live trading * Work evenings and weekends during active sports schedules Required Skills * 2-5+ years of experience in quantitative ...

Novig

Junior Sports Trader

New York, NY · On-site

$60K - $90K/yr

Junior Sports Trader $60k - $90k • meaningful equity About Novig Novig is building America's #1 ... Partner closely with quantitative analysts and trading engineers to improve trading models, tooling ...

Dime Line Trading

Live Sports Trader

Chicago, IL

Manage trading operations - confirm data feeds are available, ensure trading models launch ... Work with quantitative traders - share feedback on trading activity, analyze trading performance ...

Dime Line Trading

Always Recruiting For

Chicago, IL

$17.50 - $21.75/hr

We are always open to hiring for the following roles: - Software Developers - specifically Python - Quant Developers - Live Sports Traders - DevOps Engineers Please submit your resume and it will be ...

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Quantitative Sports Trading information

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$98K

$169.7K

$259.5K

How much do quantitative sports trading jobs pay per year?

As of Jun 12, 2026, the average yearly pay for quantitative sports trading in the United States is $169,729.00, according to ZipRecruiter salary data. Most workers in this role earn between $134,500.00 and $199,000.00 per year, depending on experience, location, and employer.

What are the main challenges quantitative sports traders face when developing and maintaining predictive models?

Quantitative sports traders often encounter challenges such as rapidly changing data, unpredictable events (like player injuries), and market inefficiencies that can impact model accuracy. Staying ahead requires constant model refinement, backtesting, and adapting strategies to new information. Additionally, traders must collaborate closely with data scientists and software engineers to ensure models are both robust and scalable in a high-pressure, time-sensitive environment.

How much money do sports traders make?

Sports traders in quantitative trading roles can earn from $50,000 to over $200,000 annually, depending on experience, performance, and the firm. Compensation often includes base salary, bonuses, and profit-sharing, with successful traders able to significantly increase their earnings through skill and risk management.

How much do quant sports traders make?

Quantitative sports traders typically earn between $80,000 and $200,000 annually, with top performers and those in senior roles earning higher salaries and bonuses. Compensation depends on experience, performance, and the trading firm's size and success, often supplemented by performance-based incentives.

What jobs pay 2000 a day?

In quantitative sports trading, professionals such as quantitative analysts or traders can earn around $2,000 or more per day through high-volume trading, algorithmic strategies, and performance-based bonuses. These roles typically require advanced skills in mathematics, programming, and data analysis, and often involve working in fast-paced financial environments. Earnings vary based on experience, performance, and the firm's size and profitability.

What is quantitative sports trading?

Quantitative sports trading involves using mathematical models, statistical analysis, and data-driven strategies to predict outcomes and make informed bets or trades in sports markets. Professionals in this field analyze large datasets, create algorithms, and develop automated systems to identify value and manage risk. This approach is similar to quantitative trading in financial markets but is applied to sports betting exchanges and sportsbooks. The goal is to consistently find profitable opportunities while minimizing losses.

What jobs make $1,000,000 a year?

In the field of quantitative sports trading, top professionals such as senior traders or hedge fund managers can earn $1,000,000 or more annually through successful trading strategies, risk management, and advanced analytical skills. These roles often require extensive experience, strong quantitative abilities, and proficiency with trading algorithms and data analysis tools.

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

To thrive as a Quantitative Sports Trader, you need strong mathematical, statistical, and analytical skills, often supported by a degree in mathematics, statistics, finance, or a related field. Proficiency with programming languages like Python or R, experience with data modeling tools, and familiarity with betting exchanges or proprietary trading platforms are typically required. Exceptional problem-solving abilities, attention to detail, and the capacity to make quick, data-driven decisions under pressure are crucial soft skills. These competencies enable traders to develop profitable strategies, manage risk effectively, and adapt to the fast-paced, dynamic environment of sports markets.
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Quantitative Sports Trading jobs near you
Infographic showing various Quantitative Sports Trading job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 50% In-person, and 50% Remote job distribution, with an average salary of $169,729 per year, or $81.6 per hour.

Sports Trading Analyst

AEG

San Francisco, CA • On-site

$70K - $120K/yr

Full-time

Posted 2 days ago

Job description

Company Overview
Swish Analytics is a sports analytics, betting, and fantasy startup building the next generation of predictive sports data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports expertise, not intuition. We are looking for team-oriented individuals with an authentic passion for accurate, predictive, real-time data who can execute in a fast-paced, creative, and continually evolving environment without sacrificing technical excellence.
Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building high-performance pricing and trading systems.
Job Description
Swish is looking for a highly analytical Sports Trading Analyst to help strengthen and scale our sports pricing and trading operation.
In this role, you will work at the intersection of sports intelligence, quantitative modelling, pricing strategy, and live market behaviour. You will help manage and improve real-time pricing across a range of sports and market types, with a particular focus on market aware price discovery, risk management and the identification of actionable trading signals from market activity.
This role is suited to someone with strong quantitative reasoning, excellent decision-making under pressure, and a deep interest in how markets are formed, odds move, and how to engineer accurate pricing in the competitive sports betting environment.
You will work in a geographically dispersed team alongside experienced traders, quants, data scientists, and engineers, with colleagues based across Europe and the US.
Duties
  • Monitor live sports markets and market activity in real time across a range of sports and market types
  • Support the calibration and refinement of prices using market signals, statistical models, competitor benchmarking, and event-driven information
  • Help improve pricing quality through the analysis of market behaviour, price sensitivity, liquidity patterns, and reaction speed to new information
  • Contribute to the development, testing, and refinement of quantitative models by applying your understanding of live market dynamics and pricing behaviour
  • Own and manage real-time trading risk, including exposure monitoring, liability controls, and disciplined decision-making across concurrent events
  • Collaborate with engineering on trading and pricing infrastructure, including API integrations, automated monitoring, alerting, anomaly detection, and execution tooling
  • Work closely with Sports Trading teams to interpret breaking news, lineups, injuries, team news, and other event-specific developments to ensure timely and accurate price updates
  • Identify model discrepancies, edge cases, and structural inefficiencies in pricing workflows, escalating and documenting findings for Data Science and Data Engineering teams
  • Help evaluate market opportunities, prioritise resources across sports and competitions, and improve operational processes as the trading function scales
  • Detect sharp or informative market activity and ensure useful signals are fed back into Swish's proprietary models and pricing systems
  • Communicate effectively with internal Sports Trading teams responsible for maintaining and improving our core sportsbook pricing models

Requirements
  • Bachelor's degree or higher in a quantitative or analytical discipline (Mathematics, Statistics, Computer Science, Economics, Engineering, Quantitative Finance, or similar), or equivalent practical experience
  • Strong grounding in probability, statistics, and expected value, with the ability to reason clearly about fair price, uncertainty, and risk
  • Hands-on experience in sports trading, sports betting, exchange-style environments, market-making, quantitative trading, or other closely related domains where fast price formation and disciplined execution matter
  • Strong understanding of sports betting fundamentals, including odds formats (decimal, fractional, American), implied probability conversion, expected value, and closing line value
  • Demonstrated ability to make high-quality decisions under time pressure with incomplete information during live events
  • Comfortable working autonomously across global event schedules, including weekends and major tournament periods
  • Fluent in English, written and spoken, with clear communication skills in a distributed and asynchronous team environment

Preferred (but not essential)
  • Track record of building and backtesting quantitative models using real historical data; GitHub, notebooks, or demonstrable analytical work is highly valued
  • Deep domain knowledge across high-turnover sporting verticals such as NBA, NFL, and Soccer
  • Understanding of relational database systems (MySQL or equivalent) for analysis of prices, outcomes, and trading decisions
  • Familiarity with market microstructure concepts such as adverse selection, inventory risk, liquidity dynamics, queue positioning, or execution quality
  • Experience using Python for quantitative research, exploratory data analysis, prototyping, or model improvement
  • Experience using modern AI tools to accelerate analysis, research, and modelling workflows

Why Join
This is an opportunity to play a meaningful role in a growing and well-resourced sports trading operation. The successful candidate will help shape process, tooling, and decision-making within a team focused on high-quality pricing, efficient execution, and long-term product excellence across multiple sports verticals.
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.

About AEG

Sourced by ZipRecruiter

Industry

Recruiting and staffing services

Company size

51 - 200 Employees

Headquarters location

Saint Louis, MO, US

Year founded

1992

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