Machine Learning Engineer, Causal Inference, Level V

What you'll do

• Build models that quantify causal impact to optimize decision-making for users, advertisers, and the business.
• Develop and productionize causal machine learning solutions like uplift modeling and heterogeneous treatment effect estimation.
• Design, analyze, and interpret A/B tests and quasi-experiments using both observational and experimental data.
• Evaluate technical tradeoffs between model complexity, bias, variance, scalability, and interpretability.
• Maintain high engineering standards by conducting code reviews and building scalable infrastructure.
• Translate complex technical insights into actionable information for non-technical partners.

What we're looking for

• Bachelor’s degree in computer science, statistics, economics, or a related technical field.
• 5+ years of post-Bachelor’s experience in machine learning with hands-on experience in causal inference or experimentation.
• Master’s degree in a technical field plus 4+ years of post-grad machine learning experience.
• PhD in a relevant technical field plus 2 years of post-grad machine learning experience.
• Proficiency in Python and common data/machine learning libraries like pandas, NumPy, scikit-learn, and CausalM.
• Experience designing and analyzing A/B tests and leveraging causal ML in production systems.
• Strong understanding of causal inference methods such as meta learners, propensity score matching, and instrumental variables.
• Experience with causal inference libraries including CausalML, EconML, or DoWhy.