Applied Scientist- Pricing

Opendoor

Quick summary

Work type
On-site
Location
Toronto, Canada
Posted
33 days ago

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Salary context

How this pay compares to similar roles

Similar $184k
$129k most similar roles pay here $236k

This listing doesn't post a salary. Most similar roles pay $141,750–$225,500.

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.

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At a glance

TL;DR · Applied Scientist- Pricing

As an Applied Scientist at Opendoor in Toronto, you will join a small, agile team focused on solving complex quantitative problems related to structural modeling, econometrics, and optimization. Your daily tasks will involve building models that enhance pricing strategies, manage portfolio risk, and optimize demand forecasting using both pre-listing and post-listing data signals. You will also develop robust optimization frameworks and design experiments to measure customer response and price elasticity. Ideal candidates possess strong Python coding skills, experience with causal inference or Bayesian modeling, and the ability to translate ambiguous business challenges into rigorous statistical models. Familiarity with distributed data processing tools like PySpark and machine learning methods is a plus. This role demands an advanced degree in a quantitative field and offers opportunities to shape both the modeling direction and its practical application within Opendoor’s valuation ecosystem.

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.
  • Translate ambiguous business challenges into rigorous modeling approaches effectively.

What we're looking for

  • Experience developing quantitative models for decision-making under uncertainty.
  • Strong Python coding skills for implementing production-quality scientific code.
  • Advanced degree in statistics, mathematics, economics, or related field.
  • Expertise in causal inference, Bayesian modeling, structural modeling, 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, or risk modeling preferred.

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