Applied Scientist- Pricing

Opendoor

Quick summary

Work type
On-site
Location
Miami, FL
Salary
$156,800–$335,000 / yr
Posted
33 days ago
Nearby
99+ roles within 25 mi

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

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Join our dynamic and innovative team as an Applied Scientist in Miami, focusing on tackling complex quantitative challenges at Opendoor. This senior-level position involves developing structural models, econometrics, optimization techniques, and decision-making frameworks under uncertainty, impacting areas like pricing, resale strategy, demand modeling, and risk management. You will build practical solutions by combining strong modeling intuition with hands-on engineering skills in Python, while working closely with Engineering, Product, and Operations to implement these models into production systems. Ideal candidates have advanced degrees in quantitative fields and experience with causal inference, Bayesian modeling, or mathematical optimization. Familiarity with real estate data and distributed data processing tools like PySpark is a plus. This role offers the opportunity to shape both the modeling direction and its application in high-stakes business decisions.

Skills

Python Pyspark Bayesian modeling Structural modeling Demand forecasting Causal inference Mathematical optimization Distributed data processing Machine learning Deep learning Statistical modeling Econometric modeling Optimization frameworks CI/CD

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 for production-quality scientific code implementation.
  • 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 messy, high-dimensional data and translate business problems into rigorous models.

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