Applied Scientist: Growth ML, Causal Inference

Open Door Inc.

Miami, Northern (FL, KY)

Hybrid

USD 120,000 - 190,000

Full time

9 days ago
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Job summary

Opendoor in Miami is seeking an Applied Scientist to tackle quantitative problems in ML, causal inference, optimization, and decision-making under uncertainty, with applications spanning marketing investment, customer acquisition, lifecycle engagement, and conversion. You will build models that forecast seller intent, estimate lifetime value, and create optimizers to guide spend.

The role blends modeling excellence with hands-on engineering to produce production-ready solutions for a

Qualifications

  • Strong Python skills and experience building production ML systems.
  • Background in classification and statistical modeling.
  • Applied experience in causal inference and experimental design.

Responsibilities

  • Build models and decision systems for profitable growth in acquisition and engagement.
  • Develop intent, conversion, and lifetime value models to optimize marketing.
  • Improve marketing measurement and optimization across channels, markets and time.
  • Apply causal inference to estimate incremental marketing impact across customers and markets.
  • Partner with cross-functional teams to deploy models into production decisions.
  • Move from research to production with ongoing evaluation.

Skills

Python
Production ML systems
Classification & statistics
Causal inference
Experiment design
Business-level communication

Education

MS/PhD in a quantitative field

Tools

PySpark

Job description

Opendoor in Miami is seeking an Applied Scientist to tackle quantitative problems in ML, causal inference, optimization, and decision-making under uncertainty, with applications spanning marketing investment, customer acquisition, lifecycle engagement, and conversion. You will build models that forecast seller intent, estimate lifetime value, and create optimizers to guide spend.

The role blends modeling excellence with hands-on engineering to produce production-ready solutions for a

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