Senior Quantitative Sports Modeler

We need to output HTML fragment with sections for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. Must not repeat job title. Must not include Job ID etc. Remove boilerplate. Must preserve all substantive info. We need to parse the posting. First paragraph: "At Underdog, we make sports more fun..." That's about company intro. Could be "About the role"? Actually "About the role" heading should contain description of role? Usually "About the role" includes overview. We'll put that content under

About the role

with paragraphs. Then "What you'll do:" list of responsibilities. We'll put under

Responsibilities

with

• items. Then "Who you are:" qualifications etc. Could be split into Requirements, Qualifications, Skills. The posting includes: "At least 5 years of experience..." etc. Also "Bonus points if you have:" etc. Also "Our target starting base salary range..." etc. Also "What we can offer you:" benefits. We need to decide sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We have Pay: salary range given. So include Pay section. Schedule: mention "Willingness to work occasional evenings and weekends during major sports periods and other time-sensitive launches, particularly around the NFL season." That's schedule expectation. So include Schedule. Benefits: list under "What we can offer you:" includes unlimited PTO, parental leave, home office stipend, virtual-first culture, Slack reactions, 401k, health plans, opportunity to celebrate teams, Claude and ChatGPT enterprise accounts. Also maybe "remote-first by design" but that's more about work arrangement; could be in Benefits or About the role. We'll keep in Benefits. Qualifications: maybe the "At least 5 years..." etc. Requirements: maybe the core qualifications? Could split but we need to avoid thin sections. Perhaps combine Requirements and Qualifications into one? But instructions: Use headings only when posting has real specific content for it. We can include both if they have distinct content. Let's see: - Requirements: maybe "At least 5 years of experience..." etc. That's a requirement. - Qualifications: maybe "Deep knowledge of probability..." etc. That's also qualification. - Skills: maybe "High proficiency in Python..." etc. Also "AI-forward mindset". Could be skills. We can separate them. But we must avoid filler like "commensurate with experience". Not present. We need to ensure we don't create sections with placeholder text. Let's extract content. About the role: The introductory paragraphs about Underdog and the role overview: "At Underdog, we make sports more fun... Founded in 2020... We've built and scaled products... Underdog isn't for everyone... We're remote-first by design... Got that dog in you? Join us. After all, winning as an Underdog is more fun." Also maybe the sentence "As a Sports Modeler at Longshot Capital, you will be a senior individual contributor..." That's more responsibilities. But we can include overview in About the role: the company description and role overview. We'll keep that. Responsibilities: bullet list from "What you'll do:" includes: - Build Simulation Based Sports Models: Design, build, and validate simulation-based predictive and pricing models across multiple sports, market types, and time horizons, including pregame and live opportunities where appropriate. - Translate sport mechanics, team and player data, correlations, and uncertainty into full outcome distributions and market probabilities rather than relying only on point estimates. - Own the model lifecycle from problem framing, data and feature design through backtesting, calibration, deployment, documentation, and ongoing production monitoring. - Apply Bayesian inference to establish and update priors as new information arrives, with a clear understanding of when the evidence supports a meaningful change in price. - Combine Model Outputs with Market Information: Understand how sportsbook prices are formed, including margin, market limits, timing, line movement, and differences in information quality across operators. - Interpret exchange prices and order books in the context of liquidity, spread, depth, flow, adverse selection, inventory, and other market-microstructure effects. - Combine internal model outputs, sportsbook signals, exchange information, and structured trader feedback into defensible estimates of fair value, while quantifying uncertainty and avoiding false precision. - Measure performance against market benchmarks and trading outcomes, diagnose systematic weaknesses, and prioritize improvements with the greatest expected commercial impact. - Use AI to Increase Development Velocity: Use AI-assisted workflows throughout research, prototyping, coding, testing, debugging, review, and documentation to move quickly from a modeling idea to a production-ready solution. - Give AI tools precise technical context, constraints, and acceptance criteria; critically evaluate their output; and retain full ownership of methodology, code quality, and correctness. - Write clear, maintainable Python and use Git and GitHub effectively for branching, pull requests, code review, version control, testing, and collaborative development. - Build reusable modeling components and improve runtime, reliability, and reproducibility so the team can iterate quickly without accumulating avoidable technical debt. - Partner Across Trading Engineering and Product: Build a tight feedback loop with traders, turning real-time market observations and recurring pricing issues into testable hypotheses and concrete model improvements. - Work with engineers to move models into production reliably, define data and infrastructure requirements, and resolve performance or operational issues when they arise. - Support new sports, markets, and product launches by assessing model feasibility, identifying key sources of uncertainty, and delivering pricing solutions on practical timelines. - Explain complex modeling choices, limitations, and tradeoffs clearly to technical and non-technical partners, and incorporate constructive challenge into the work. - Provide High Level Technical Leadership: Raise the technical bar through model and code reviews, clear design documentation, thoughtful challenge, and hands-on support for other modelers. - Help define modeling standards and reusable approaches across sports while recognizing when a sport or market requires a purpose-built solution. - Operate effectively when priorities change, information is incomplete, and timelines are compressed; make sound decisions, communicate risks, and keep work moving. We'll turn each bullet into
• . Might be many but okay. Requirements: "At least 5 years of experience in sports modeling, quantitative research, predictive analytics, or a closely related field, including substantial ownership of production models." Also maybe "A demonstrated record of building simulation-based sports models that produce calibrated probabilities and can be evaluated against real markets or real-money decisions." That's also requirement. Qualifications: "Deep knowledge of probability, statistical modeling, Bayesian inference, simulation, backtesting, calibration, and model validation. Strong understanding of both sportsbook market dynamics and exchange-market dynamics, and the judgment to combine market information with independent model estimates. High proficiency in Python and practical experience using Git and GitHub in a collaborative production-code environment. An AI-forward mindset and advanced, demonstrable use of AI coding and research tools, including the technical depth to direct them precisely and verify their work rather than accept output at face value. Strong ownership, attention to detail, and the ability to thrive in a fast-paced environment with frequent change, incomplete information, and competing priorities. Clear communication and strong cross-functional instincts, with the ability to work effectively with modelers, traders, engineers, product partners, and business leaders." Skills: maybe the bonus points? Actually bonus points are extra qualifications. Could be considered "Preferred Qualifications". But we have Skills heading; we could put the bonus points there. However the instructions: Use headings only when posting has real specific content for it. Bonus points are specific content. So we can put them under Skills. But we also have "Willingness to work occasional evenings and weekends during major sports periods and other time-sensitive launches, particularly around the NFL season." That's schedule. Pay: "Our target starting base salary range for this position is between $135,000 and $185,000, plus target equity. Final compensation will be determined based on a variety of factors including relevant experience, skills and expertise, interview performance, and other market considerations." Benefits: list from "What we can offer you:" includes: - Unlimited PTO for full-time employees (we're extremely flexible with the exception of the first few weeks before and into the NFL season) - 16 weeks of fully paid parental leave - Home office stipend - A virtual-first culture, but we come together a few times per year in person for offsites, trainings, hackathons, and more - Slack reactions for every occasion - 401k with up to 5% employer match - Company paid health, dental, vision plan options for employees and dependents, FSA, and more - Opportunity to celebrate your favorite sports teams through local meetups and company-sponsored events - Claude and ChatGPT enterprise accounts Also maybe remote-first design but that's more about work arrangement; could be in Benefits as "remote-first by design". But we already have virtual-first culture. We'll include those. Now schedule: we have the willingness to work occasional evenings and weekends. Now we need to output HTML fragment only, no markdown fences. We'll produce:

  About the role

  ... paragraphs ...

  Responsibilities

  • ...
  ...

  Requirements

  • At least 5 years of experience in sports modeling, quantitative research, predictive analytics, or a closely related field, including substantial ownership of production models.
  • A demonstrated record of building simulation-based sports models that produce calibrated probabilities and can be evaluated against real markets or real-money decisions.

  Qualifications

  • Deep knowledge of probability, statistical modeling, Bayesian inference, simulation, backtesting, calibration, and model validation.
  • Strong understanding of both sportsbook market dynamics and exchange-market dynamics, and the judgment to combine market information with independent model estimates.
  • High proficiency in Python and practical experience using Git and GitHub in a collaborative production-code environment.
  • An AI-forward mindset and advanced, demonstrable use of AI coding and research tools, including the technical depth to direct them precisely and verify their work rather than accept output at face value.
  • Strong ownership, attention to detail, and the ability to thrive in a fast-paced environment with frequent change, incomplete information, and competing priorities.
  • Clear communication and strong cross-functional instincts, with the ability to work effectively with modelers, traders, engineers, product partners, and business leaders.

  Skills

  • Direct experience building models for a market maker, proprietary trading firm, prediction-market business, exchange, or sportsbook trading desk.
  • Experience trading on exchanges or building pricing, liquidity, or automated market-making solutions for exchange venues.
  • Experience with in-play or other low-latency models where real-time state, reliability, and execution speed materially affect performance.
  • Experience designing modeling infrastructure, cloud-compute solutions, backtesting systems, production monitoring, or GitHub-based CI/CD pipelines and workflows.
  • Experience optimizing computationally intensive code or building performance-critical components in C++ or Rust.
  • A genuine passion for sports and curiosity about how information moves prices.

  Benefits

  • Unlimited PTO for full-time employees (we're extremely flexible with the exception of the first few weeks before and into the NFL season)
  • 16 weeks of fully paid parental leave
  • Home office stipend
  • A virtual-first culture, but we come together a few times per year in person for offsites, trainings, hackathons, and more
  • Slack reactions for every occasion
  • 401k with up to 5% employer match
  • Company paid health, dental, vision plan options for employees and dependents, FSA, and more
  • Opportunity