Causal Data Scientist for Product & Experiments

Nubeero Limited; Northern (KY); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Bonus eligibility

Equity

Employee travel credits

Job summary

Airbnb is seeking a Data Scientist with strong causal inference expertise to join the Community Support Data Science team. You will design and implement causal inference frameworks, build causal ML models, and quantify the impact of CS product changes.

The role emphasizes collaboration with Product, Design, Engineering, Operations, and Analytics to drive data-informed decisions. You will translate complex findings into actionable insights, communicate recommendations to leadership, and

Qualifications

• 2+ years in quantitative analysis
• Strong causal inference and experimental design knowledge
• Hands-on experience deploying models to production
• Excellent communication across technical levels
• Ability to translate findings into business impact

Responsibilities

• Design causal inference frameworks and experiments to measure interventions
• Build causal ML models to optimize budgets and outcomes
• Conduct analyses to quantify long-term effects of product changes
• Communicate insights to leaders to drive data-driven decisions
• Collaborate with Product, Design, Engineering, and Analytics across teams

Skills

Causal inference

Bayesian modeling

Experiment design

Python/R

SQL

Data storytelling

Cross-functional collaboration

Education

Master's degree (statistics/economics/CS)

PhD in a quantitative field

Tools

Python

R

SQL

Job description

Airbnb is seeking a Data Scientist with strong causal inference expertise to join the Community Support Data Science team. You will design and implement causal inference frameworks, build causal ML models, and quantify the impact of CS product changes.

The role emphasizes collaboration with Product, Design, Engineering, Operations, and Analytics to drive data-informed decisions. You will translate complex findings into actionable insights, communicate recommendations to leadership, and