Applied Scientist- Pricing

Opendoor

Quick summary

Work type
On-site
Location
Seattle, WA
Salary
$156,800–$335,000 / yr
Posted
33 days ago

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $184k
This role $246k
$115k most similar roles pay here $359k

This role pays more than 84% of similar roles. Most pay $141,750–$225,500 — the shaded band above. At the midpoint, this role pays about $246k versus about $184k for comparable roles.

Based on 240 similar postings.

Employer

About Opendoor

Opendoor is a digital real estate marketplace that buys and sells homes directly to consumers, simplifying the home selling and buying experience through instant offers and transparent pricing. Industry: Real Estate Technology & iBuying

Opendoor currently has 42 open roles on FindRole.

Listed pay typically runs $156,800–$335,000 across 8 roles with salary data.

Most-posted roles

View all roles at Opendoor

At a glance

TL;DR · Applied Scientist- Pricing

Join our Applied Scientist team in Seattle as we tackle complex quantitative challenges at Opendoor. This senior-level position focuses on structural modeling, econometrics, and optimization to enhance pricing strategies, demand forecasting, and risk management. You will develop robust models that drive decision-making across various business areas, from valuation systems to inventory optimization. Key responsibilities include building predictive models for post-listing demand, estimating price elasticity, and designing experiments to measure customer response. Ideal candidates have advanced degrees in quantitative fields and strong Python coding skills, along with experience in causal inference, Bayesian modeling, or mathematical optimization. Familiarity with distributed data processing tools like PySpark is a plus. This role offers the chance to work on high-impact projects that require rigorous statistical methods and practical implementation in a fast-paced environment.

What you'll do

  • Develop quantitative models to support decision-making under uncertainty in pricing and strategy.
  • Build demand and conversion models using pre-listing and post-listing signals.
  • Design optimization frameworks balancing margin, conversion, and risk objectives.
  • Apply statistical and econometric techniques to complex business problems.
  • Create experiments to measure price elasticity and customer response accurately.
  • Implement production-quality scientific code from prototypes in Python.

What we're looking for

  • Experience developing quantitative models for real-world decision-making under uncertainty.
  • Strong Python coding skills and ability to implement production-quality scientific code.
  • Advanced degree in statistics, mathematics, economics, or a related quantitative field.
  • Expertise in causal inference, Bayesian modeling, structural modeling, demand forecasting, pricing science, or optimization.
  • Ability to work with high-dimensional real-world data and translate business problems into rigorous models.
  • Experience in pricing, marketplace modeling, revenue management, supply/demand systems, inventory optimization, or risk modeling.

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