Quantitative Developer (Affiliated Trading)

As a Quantitative Developer at Kalshi, your main responsibilities will include planning, building, and maintaining the autonomous systems that execute our market-making strategies. You will deal with large sports datasets, build forecasting models, generate prices using bookmaker odds, and ensure our systems function with low latency. You'll also collaborate closely with the research team to implement and refine our algorithms.

To qualify for the Quantitative Developer position at Kalshi, you should have 3-7 years of experience in building distributed and performance engineering systems. A strong grasp of theoretical trading system design and programming languages like Python or R for statistical modeling is also required. Experience in the sports or betting sectors will be advantageous.

For a Quantitative Developer at Kalshi, proficiency in programming languages such as Python or R is essential. These languages are used extensively for statistical modeling and will be crucial in developing the systems that support our market-making strategies.

At Kalshi, we encourage our Quantitative Developers to carve their paths as the company grows. Your role will evolve with the company, giving you plenty of opportunities to influence system designs and contribute to significant projects. We believe in empowering our team members, which drives personal and professional growth.

Kalshi's culture is rooted in meritocracy, ownership, and the pursuit of meaningful work. As a Quantitative Developer, you'll be part of a team that values innovative thinking and collaboration, where your contributions are respected and celebrated. We hire talented individuals and trust them to push boundaries while enjoying the journey together.

When answering this question, detail your past projects that involved distributed systems, focusing on your specific role, the challenges faced, and how you overcame them. Use metrics to showcase the impact of your work.

Describe the forecasting model in terms of its purpose, the data used, and the algorithms implemented. Highlight any successes or improvements in accuracy that resulted from your model.

Discuss your experience with performance monitoring tools and techniques. Mention specific examples of optimizations you've made in the past and how they improved system responsiveness.

Illustrate your approach to data analysis, including how you interpret data insights to drive decisions. Use specific examples where analysis led to impactful outcomes in your previous roles.

Provide a detailed account of a high-pressure scenario, focusing on your problem-solving skills, your approach to managing stress, and how you ensured effective communication within your team.

Explain your prioritization method, whether it’s deadline-driven, impact-focused, or agile in nature. Offer examples of how this approach has led to effective project completion in past roles.

Talk about the tools you're proficient in, such as Python or R, and justify why you prefer them, backing it up with examples of successful implementations or personal projects.

Discuss your methods for staying informed, whether through research papers, courses, or networking. Highlight any specific trends you've recently integrated into your work.

Share your thoughts on current challenges based on industry observations, focusing on technology advancements, regulatory changes, and data privacy issues, and how you would tackle these challenges.

Express your enthusiasm for Kalshi's mission and how your skills align with the company’s goals. Relate your passion for prediction markets and quantify how you can contribute to the team.