Machine Learning Engineering Manager
1 week ago
Snap Inc. is a technology company that believes the camera presents the greatest opportunity to improve the way people live and communicate. We contribute to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together.
Our ProductsOur three core products are Snapchat, a visual messaging app that enhances your relationships with friends, family, and the world; Lens Studio, an augmented reality platform that powers AR across Snapchat and other services; and our AR glasses, Spectacles.
Our Engineering TeamsOur engineering teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters 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.
About the RoleWe're looking for a Machine Learning Engineering Manager to join the Causal Measurement team, part of the monetization team at Snap. As a Machine Learning Engineering Manager, you will lead a team of machine learning engineers, data scientists, and software engineers in developing and optimizing systems to conduct lift measurement and incrementality optimization.
Responsibilities- Lead a team of machine learning engineers, data scientists, and software engineers in developing and optimizing systems to conduct lift measurement and incrementality optimization.
- Define the overall architecture of the system, ensuring scalability, performance, and reliability.
- Conduct lift analysis to evaluate the effectiveness of marketing campaigns, product changes, and other interventions.
- Drive innovation in experimental design by implementing and optimizing techniques like hypothesis testing, variance reduction, Bayesian inference, multi-arm bandits, synthetic control, and counterfactual frameworks with robust estimation methods.
- Advocate for best practices in causal inference, experimental design, and end-to-end pipeline optimization across the organization.
- Chair the Experimentation Council and collaborate with cross-functional teams to ensure high-quality, actionable insights through rigorous A/B testing, uplift modeling, quasi-experimental designs, and advanced causal inference methods.
- Collaborate with senior leadership and cross-functional teams to translate complex data insights into strategic recommendations.
- Perform methodology, design, and code reviews to raise technical excellence bar.
- Deep understanding of causal measurement approaches, algorithms, and their application to lift measurement.
- Experience setting the direction for teams focused on developing experiment-based measurement.
- Strong management and mentorship skills, fostering a collaborative and innovative team culture.
- Excellent verbal and written communication skills, with meticulous attention to detail.
- Ability to effectively collaborate with stakeholders at all levels, both internally and externally.
- Proficiency in managing and solving ambiguous problems.
- Deep understanding of experimental design, A/B testing, and causal inference methods (e.g., propensity score matching, instrumental variables, regression discontinuity).
- Familiarity with techniques like hypothesis testing, variance reduction, Bayesian inference, multi-arm bandits, synthetic control, and counterfactual frameworks with robust estimation methods.
- Experience with large-scale data sets and big data technologies.
- Experience with ad ecosystem and working with cross-functional stakeholders.
- Experience working with distributed systems.
- Experience working with machine learning, ranking infrastructures, and system designs.
- Ability to proactively learn new concepts and apply them at work.
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