Machine Learning Engineer, Causal Inference, Level 5

Snap Inc is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together.

The Company operates Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world, and Specs Inc., a wholly-owned subsidiary dedicated to making computing more human, in addition to Bitmoji, Saturn, and other digital services.

Snap Engineering teams build fun and technically sophisticated products that reach hundreds of millions of Snap chatters around the world, every day. We’re deeply committed to the well-being of everyone in our global community, which is why our values are at the root of everything we do. We move fast, with precision, and always execute with privacy at the forefront.

Design and build models that quantify causal impact, optimize decision-making, and drive value for users, advertisers, and the business

Develop and product ionize 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

Strong understanding of causal inference and modern approaches to estimating treatment effects (e.g., meta learners, propensity score matching, 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, Num Py, scikit-learn, CausalM 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

Advanced degree (MS/PhD) in a quantitative field such as statistics, data science, computer science, economics, or operations research

Background in deploying models in production settings and working with ML or experimentation infrastructure

Deep understanding of experimentation nuances, including intent-to-treat (ITT) vs. ghost ad methodologies, and the trade-offs between frequentist and Bayesian inference for decision-making under uncertainty

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