Applied Science, Growth

Opendoor

Confirmed live today Low trust

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On-site
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Posted
28 days ago
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Confirmed live today

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How this pay compares to similar roles

Similar $202k
$136k $267k
below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $161,496–$241,906.

Based on 240 similar postings.

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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 21 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 Science, Growth

The Applied Science- Growth role involves joining a small, nimble team to solve complex quantitative problems within a low-margin, high-stakes business environment. You will build machine learning models and decision systems to support profitable growth by improving customer acquisition, lifecycle engagement, and conversion. Daily responsibilities include predicting seller intent, estimating customer lifetime value, and developing marketing mix models to optimize spend allocation. You will apply causal inference to experimental and observational data to estimate incremental marketing impacts while managing confounding and selection bias. Required skills include Python, production ML systems, classification, and statistical modeling. You must be able to translate findings into recommendations for technical and business partners. The work focuses on optimizing marketing investment and identifying customer interactions that drive incremental growth in a marketplace with long decision cycles.

What you'll do

  • Build models and decision systems to support profitable growth through improved customer acquisition and engagement.
  • Develop intent, conversion, and customer lifetime value models to optimize engagement strategies.
  • Build marketing measurement and optimization systems to guide budget allocation across channels and markets.
  • Apply causal inference to experimental and observational data to estimate incremental marketing impact.
  • Translate research and prototypes into production-ready systems that influence real-time business decisions.
  • Own the ongoing evaluation and improvement of models based on both predictive performance and business outcomes.

What we're looking for

  • Strong Python skills with experience building maintainable software and production ML systems.
  • Experience taking predictive models from problem definition through deployment, evaluation, and iteration.
  • Strong foundation in classification and statistical modeling.
  • Applied experience in causal inference and experimental design, including estimating incremental effects and reasoning about bias.
  • Ability to evaluate models using both predictive performance and business outcomes.
  • Comfort working with imperfect data and translating findings into clear recommendations for technical and business partners.
  • Advanced degree (MS or PhD preferred) in statistics, economics, computer science, mathematics, operations research, or a related quantitative field.
  • Experience in media targeting, marketing mix modeling, or customer acquisition (preferred).

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