Machine Learning Engineer, Snap

• Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., T-learner, S-learner, X-learner, DR-learner, propensity scoring, instrumental variables)
• Experience with applied data science, including A/B testing, uplift modeling, and experimentation infrastructure
• Proficient in Python and common data/machine learning libraries (e.g., pandas, NumPy, scikit-learn, CausalML, EconML, PyTorch, etc.)
• Skilled at solving open-ended problems with a mix of statistical thinking and engineering pragmatism
• Comfortable working independently and collaborating across cross-functional teams
• Strong communication and mentorship skills; able to translate technical insights for non-technical partners
• Bachelor’s degree in computer science, statistics, economics, or a related technical field, or equivalent practical experience
• 5+ years of post-Bachelor’s experience in machine learning, with hands-on experience in causal inference or experimentation; or Master’s degree in a technical field + 4+ year of post-grad machine learning experience; or PhD in a relevant technical field + 2 years of post-grad machine learning experience
• Demonstrated experience building models to support product decision-making and policy evaluation through causal techniques
• Experience designing and analyzing online experiments (A/B tests) and leveraging causal ML in production systems

Desirable

• Snap Engineering teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters around the world, every day
• Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business
• Develop and productionize causal machine learning solutions (e.g., uplift modeling, heterogeneous treatment effect estimation) using observational and experimental data
• Design, analyze, and interpret A/B tests and quasi-experiments; collaborate closely with product and engineering partners to shape experimentation strategies
• Evaluate technical tradeoffs between model complexity, bias/variance, scalability, and interpretability
• Conduct code reviews, maintain high engineering standards, and build scalable, maintainable infrastructure
• Contribute to rapid iteration cycles while ensuring methodological rigor