Staff Quantitative Researcher, Pricing

Alt · posted Oct 25, 2026

Open to candidates in United States

$185k–$220k · Full-time · Mid-level

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What this job asks for

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Must have

  • •Track record of building pricing/forecasting models in production
  • •Familiarity with tree-based models and newer approaches
  • •Experience combining multiple models or signals
  • •Market prediction with alternative data
  • •Statistical rigor: experimental design, backtesting
  • •Expert SQL and Python
  • •Hands-on work building with AI

Nice to have

  • •Depth in non-traditional or illiquid assets
  • •Clear communication to non-technical audiences

Tools and technologies

SQL, Python, large language models, time-series foundation models, graph neural networks

Read the full posting

Alt is unlocking the value of alternative assets, starting with the $5 B trading-card market. We let collectors buy, sell, vault, and finance their cards in one place and we are backed by leaders at Stripe, Coinbase, Seven Seven Six, and pro athletes like Tom Brady and Giannis Antetokounmpo. Our next frontier is real-time pricing at scale—the Alt Value that powers every trade, loan, and product on the platform.

The North Star

$ Automatically Priced

The Role

Alt Value, our pricing engine, shapes what collectors see in the app and what they can borrow against their cards. This role makes Alt Value more accurate.

This is a hands-on seat. You'll find new signals, build them into our pricing, and decide how our models are used.

What this role is not: a reporting role or a management track.

What it is: testing whether new data predicts price moves, investigating unusual sales, explaining price gaps between marketplaces, and deciding which model to trust when two disagree.

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

The people who do well here are obsessed with making every decision with numbers, even the uncertainty is quantified.

What You'll Own

  • New data signals. Identify new signals, from market sentiment to sports outcomes, and validate their predictive power through rigorous backtesting.

  • Market manipulation detection. Identify potential market manipulation and unusual activity, and keep it from distorting our valuations.

  • Cross-market pricing. Design models for an inefficient market, where the same card can be mispriced from one marketplace to the next.

  • Model selection and blending. Evaluate our pricing models and decide which to use, or how to blend them, weighing the trade-offs.

  • Release and routing decisions. Decide when a new model version goes live and when to switch routing.

  • The bets and the trade-offs. Set your own research priorities, work with Expert Pricers, data scientists, and machine learning engineers, and communicate the reasoning behind price movements.

What Great Looks Like (6 Months)

  • Pricing accuracy has measurably improved.

  • New data sources have been tested, and the useful ones are in production.

  • Potential manipulation is weighted in our valuations.

  • Model selection and blending are backed by evidence.

  • At least one new tool or method has been adopted into our pricing.

  • Expert Pricers rely on your work.

How You'll Use AI Here

We're AI-native by default. You'll stay on top of new models, tools, and techniques, and bring in what improves our pricing.

What You Bring

  • A track record of building pricing or forecasting models in production, and 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 , such as collectibles, art, real estate, or alternative credit. We're reading the work, not the years.

  • Experience combining multiple models or signals , through portfolio construction, forecast combination, or ensemble methods.

  • Market prediction with alternative data , including unstructured sources such as text and sentiment.

  • Statistical rigor : experimental design, backtesting, and noisy or sparse data.

  • Expert SQL and Python.

  • Hands-on work building with AI , such as agents or internal tools that others rely on.

  • Clear communication , with a record of explaining complex work to non-technical audiences.

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.

How We'll Interview You

Before your first conversation, download the Alt app, place a bid, and list a card. Every round assumes you have.

  • Recruiter screen, 25 minutes. Your background, motivation, and compensation.

  • Hiring manager, 45 minutes. Your past work and approach to pricing problems.

  • Take-home case study, not timed. A pricing exercise you complete on your own schedule.

  • Case study presentation, 60 minutes. If your case study moves forward, you present it to the team, followed by a group interview.

  • Culture interview, 30 minutes. How you work, and why Alt.

  • Executive review, 30 minutes. A closing conversation.

  • Reference check, 15 minutes , with a former manager.

Compensation

Base salary: $185,000 to $220,000 plus equity. Offers may vary based on experience, location, and other factors.

What we offer:

For full-time Employees

  • We cover 85% of your medical, dental, and vision, and up to 50% for dependents. HSA/FSA available

  • Generous PTO that people actually take

  • Parental leave at full salary

  • $200/month wellness + $100/month home office stipend

  • Free DoorDash Pass membership, because dinner shouldn't be a decision

  • Remote-first (for most positions), with WeWork access when you want a room with other humans

  • 401(k)

  • Alt Equity to all full-time employees

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