Causal ML Engineer — Experiments, Impact & Equity

Snap Inc.; New York (NY); In loco; USD 209.000 - 313.000; Tempo pieno

Vantaggi offerti da questo lavoro

Paid parental leave

Comprehensive medical coverage

Mental health support

RSU equity opportunities

Descrizione del lavoro

Snap Inc. in the United States is seeking a Machine Learning Engineer to join Snap Inc!

Design and productionize causal ML solutions (e.g., uplift modeling, heterogeneous treatment effect estimation) using observational and experimental data, and collaborate with product and engineering partners on experimentation strategies. This role emphasizes rigorous methodology, scalable infrastructure, and strong communication across cross-functional teams; candidates should have advanced degrees or

Competenze

• Strong foundation in causal inference and treatment effect estimation.
• Experience with A/B testing, uplift modeling and experimentation.
• Proficiency in Python with data science libraries.

Mansioni

• Design and build causal ML models to quantify impact and optimize decisions.
• Develop production-ready causal ML solutions using observational and experimental data.
• Design, analyze, and interpret A/B tests and quasi-experiments with partners.
• Evaluate tradeoffs between model complexity, bias, variance, and scalability.
• Contribute to scalable infrastructure and code reviews with engineering teams.
• Mentor teammates and communicate insights to non-technical stakeholders.

Conoscenze

Causal inference

Experimentation

Python

Data science

Communication

Mentorship

Cross-functional

Formazione

Bachelor's degree

Master's degree

PhD

Strumenti

CausalML

EconML

DoWhy

Descrizione del lavoro

Snap Inc. in the United States is seeking a Machine Learning Engineer to join Snap Inc!

Design and productionize causal ML solutions (e.g., uplift modeling, heterogeneous treatment effect estimation) using observational and experimental data, and collaborate with product and engineering partners on experimentation strategies. This role emphasizes rigorous methodology, scalable infrastructure, and strong communication across cross-functional teams; candidates should have advanced degrees or