Staff Quantitative Researcher, Pricing

Overview

This role supports Alt Value, the pricing engine that determines what collectors see in the app and what they can borrow against their cards. The position is hands-on and focused on improving pricing accuracy through testing new signals, investigating pricing anomalies, and making decisions about which models to trust when they disagree.

You will report to the Head of Pricing Automation and work closely with Expert Pricers, data scientists, and machine learning engineers.

Responsibilities

• New data signals: Identify new signals (e.g., market sentiment, sports outcomes) and validate predictive power through rigorous backtesting.
• Market manipulation detection: Identify potential manipulation and unusual activity to prevent distortion of valuations.
• Cross-market pricing: Build models for inefficient markets where the same card can be mispriced across marketplaces.
• Model selection and blending: Evaluate pricing models and decide which to use, or how to blend them, weighing trade-offs.
• Release and routing decisions: Decide when a new model version goes live and when to switch routing.
• Research priorities and communication: Set research priorities, partner with Expert Pricers, data scientists, and machine learning engineers, and clearly communicate the reasoning behind price movements.

What Great Looks Like (6 Months)

• Pricing accuracy has measurably improved.
• New data sources have been tested, and useful ones are in production.
• Potential manipulation is weighted in valuations.
• Model selection and blending are backed by evidence.
• At least one new tool or method is adopted into pricing.
• Expert Pricers rely on your work.

Requirements

• Track record of building pricing or forecasting models in production, including familiarity with tree-based models and newer approaches such as large language models, time-series foundation models, and graph neural networks.
• Depth in non-traditional or illiquid assets (e.g., collectibles, art, real estate, alternative credit). The work emphasizes reading the research rather than years.
• Experience combining multiple models or signals (e.g., portfolio construction, forecast combination, ensemble methods).
• Ability to perform market prediction with alternative data, including unstructured sources such as text and sentiment.
• Statistical rigor, including experimental design and backtesting, including work with noisy or sparse data.
• Expert SQL and Python.
• Hands-on experience building AI systems (e.g., agents or internal tools) that others rely on.
• Clear communication skills, including explaining complex work to non-technical audiences.

Preferred Qualifications / Backgrounds That Fit

• Quantitative research or portfolio construction at an investment firm
• Statistical or cross-market arbitrage
• Sports betting or prediction market pricing
• Credit or insurance underwriting
• Marketplace pricing or trust and safety

Compensation & Benefits

• Base salary: $185,000 to $220,000 plus equity. Offers may vary based on experience, location, and other factors.
• Full-time employee benefits:
  • Employer coverage of 85% of medical, dental, and vision; up to 50% for dependents (HSA/FSA available)
  • Generous PTO
  • Parental leave at full salary
  • $200/month wellness + $100/month home office stipend
  • Free DoorDash Pass membership
  • 401(k)
  • Alt Equity to all full-time employees

Location

Remote-first (for most positions), with WeWork access when a room with other humans is needed.