Applied Scientist, Pricing

Opendoor

Confirmed live yesterday Low trust

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Work type
On-site
Location
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Posted
122 days ago
Freshness
Confirmed live yesterday

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Salary context

How this pay compares to similar roles

Similar $205k
$146k most similar roles pay here $261k

This listing doesn't post a salary. Most similar roles pay $162,375–$247,718.

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 31 open roles on FindRole.

Listed pay typically runs $156,800–$335,000 across 6 roles with salary data.

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At a glance

TL;DR · Applied Scientist, Pricing

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As an Applied Scientist- Pricing, you will join a nimble team focused on solving complex quantitative problems within the valuation and pricing ecosystem. You will build structural models, develop optimizers, and create demand and conversion models using pre-listing and post-listing signals to improve decisions regarding pricing, resale strategy, and portfolio risk management. Your daily work involves translating ambiguous business problems into rigorous modeling approaches, designing experiments to quantify price elasticity, and developing production-quality scientific code. You will utilize Python, PySpark, and advanced techniques including causal inference, Bayesian modeling, structural modeling, demand forecasting, pricing science, and mathematical optimization. The role addresses high-stakes challenges in a low-margin business where you must balance objectives like margin, conversion, and risk while navigating messy, high-dimensional data to move from prototype to production-ready components for the real estate marketplace.

What does a Applied Scientist earn?

Median $208800 from 47 postings across 10 companies.

See salary data

Skills

Python Causal Inference Bayesian Modeling Structural Modeling Mathematical Optimization Pyspark Machine Learning Deep Learning LLMs VLMs Econometrics Demand Forecasting Pricing Science Data Processing Statistics

What you'll do

  • Build quantitative models to improve pricing, resale strategy, and portfolio risk management.
  • Develop demand and conversion models using pre-listing and post-listing signals.
  • Design optimization frameworks that balance margin, conversion, and risk objectives.
  • Apply structural modeling, econometrics, and mathematical techniques to solve complex business problems.
  • Design experiments to quantify price elasticity and customer response.
  • Translate ambiguous business problems into rigorous modeling approaches using high-dimensional data.
  • Develop production-quality scientific code in Python to move models from prototype to production.
  • Partner with cross-functional teams to integrate models into systems that influence real-time decisions.

What we're looking for

  • Experience developing quantitative models for decision-making under uncertainty.
  • Proficiency in Python with the ability to implement production-quality scientific code.
  • Expertise in causal inference, Bayesian modeling, structural modeling, demand forecasting, pricing science, or mathematical optimization.
  • Ability to translate ambiguous business problems into rigorous modeling approaches using messy, high-dimensional data.
  • Advanced degree (MS or PhD preferred) in a quantitative discipline like statistics, mathematics, economics, operations research, or computer science.
  • Strong communication and collaboration skills to convey technical ideas to cross-functional stakeholders.
  • Experience in pricing, marketplace modeling, revenue management, supply/demand systems, inventory optimization, or risk modeling.
  • Familiarity with distributed data processing tools (Pyspark) and machine learning methods including LLMs or VLMs.

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