Quantitative Analyst

Work side-by-side with our risk team to analyze market trends, build predictive models, and turn data into trading decisions.

RTK Tickets is a fast-moving, data-driven brokerage at the intersection of live entertainment, technology, and trading. We provide liquidity in live event markets where prices, demand, and inventory can change by the minute.

As a Quantitative Analyst, you will help turn data into decisions. Working side-by-side with our risk team, you will analyze market trends, build predictive models, and support the internal tools that drive how we buy and sell tickets.

You will learn how live markets operate—where the data comes from, how pricing decisions are made, and how we manage risk across thousands of events. You will also help test and improve the systems that power our daily operations, ensuring the team understands their value and uses them effectively.

This is a hands-on role combining analysis, communication, and creativity, with direct exposure to trading strategy, pricing decisions, and measurable business results.

Your day-to-day work will include:

• Analyze real-time and historical datasets to uncover pricing trends, inefficiencies, and trading opportunities.
• Help build and refine models that forecast demand and optimize offers.
• Support the risk team with real-time data analysis, performance tracking, and exposure monitoring.
• Evaluate market supply and demand, competing listings, recent transactions, and time until an event to inform pricing recommendations.
• Analyze inventory performance and pricing strategies to identify opportunities to improve execution and profitability.
• Assist with backtesting, scenario analysis, and measuring the effectiveness of trading and repricing strategies.
• Communicate and document updates to internal tools and processes.
• Collaborate with engineers to test enhancements and refine system performance.
• Present findings and trading insights clearly to technical and non-technical teammates.

You will work closely with the risk team and partner with engineering and operations. We value curiosity about markets, analytical rigor, clear communication, and using AI and automation to turn insights into practical improvements.

You will gain hands-on experience with fast-changing live event markets, the models and tools behind pricing decisions, and the trade-offs involved in risk and inventory management. Your analysis will help the team evaluate opportunities and improve how it operates.

• Strong analytical foundation, self-starter, and curiosity about markets.
• Proficiency in Python, SQL, and Excel;
  Go and R are a plus.
• Bachelor’s degree in Computer Science, Applied Mathematics, Physics, Finance, Economics, Statistics, or a related field.
• 1–3 years of experience in quantitative, data, or analytical roles.
• Interest in product management, process improvement, or analytics tooling.
• Clear communication skills—able to explain complex ideas simply and confidently.
• Excitement about AI, automation, and cutting-edge technology.
• Detail-oriented, organized, and eager to learn in a fast-paced environment.

• Exposure to trading, quantitative research, pricing analytics, sports betting, or prediction markets.
• Familiarity with backtesting, price optimization, inventory exposure, or risk-adjusted performance analysis.
• Interest in live events, sports analytics, and market-driven pricing.

Base salary: $90,000–$120,000 annually, with opportunity for performance-based bonuses.

• Health and dental insurance
• 401(k) retirement plan with company matching
• Tickets to select live events

This is an in-person role in Boston’s Seaport District. Applicants must live in or be willing to relocate to the Greater Boston area.

Our interview process begins with an initial conversation with our HR team, followed by introductory discussions with the hiring team. Candidates then complete a Python-based exercise evaluating analytical thinking and technical proficiency, followed by final conversations with the hiring manager and senior leadership.