Causal Machine Learning Data Jobs

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Machine Learning Engineer, Causal Inference, Level 5

Snapchat Los Angeles, CA

Los Angeles, CA

Today

$178k - $313k/yr Full time Causal Inference Causal Machine Learning Causal Ml

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Job Description

Snap is hiring a Machine Learning Engineer focused on causal inference to design, build, and productionize causal machine learning models. This role also supports experimentation strategy and rigorous evaluation of technical tradeoffs across modeling, measurement, and decision-making.

Location and Work Style

Los Angeles, CA (onsite)
Snap uses a “default together” approach
Team members are expected to work in an office 4+ days per week

Salary and Compensation

Salary range: USD 178,000 - 313,000 per year
Zone A (CA, WA, NYC): $209,000 - $313,000 base
Zone B: $199,000 - $297,000 base
Zone C: $178,000 - $266,000 base
Eligible for equity in the form of RSUs
Starting pay may be negotiable within the salary range

Responsibilities

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 such as uplift modeling and heterogeneous treatment effect estimation using observational and experimental data
Design, analyze, and interpret A/B tests and quasi-experiments, partnering with product and engineering teams to shape experimentation strategies
Evaluate technical tradeoffs across model complexity, bias/variance, scalability, and interpretability
Perform code reviews, maintain high engineering standards, and build scalable, maintainable infrastructure
Contribute to rapid iteration cycles while ensuring methodological rigor

Requirements

Strong causal inference understanding, including modern approaches to estimating treatment effects (for example: meta learners, propensity score matching, instrumental variables)
Applied data science experience, including A/B testing, uplift modeling, and experimentation infrastructure
Proficiency in Python and common libraries such as pandas, NumPy, scikit-learn, and causal inference tooling
Ability to solve open-ended problems with a blend of statistical thinking and engineering pragmatism
Comfort working independently and collaborating with cross-functional teams
Strong communication and mentorship skills, including translating technical insights for non-technical partners
Bachelor’s degree in computer science, statistics, economics, or a related technical field (or equivalent practical experience)
Experience profile: 5+ years post-Bachelor’s in machine learning with hands-on causal inference or experimentation, or Master’s + 4+ years post-grad machine learning experience, or PhD + 2 years post-grad machine learning experience
Demonstrated experience building causal models to support product decision-making and policy evaluation
Experience designing and analyzing online experiments and leveraging causal ML in production systems

Technologies

Python
pandas
NumPy
scikit-learn
CausalM
CausalML
EconML
DoWhy

Benefits

Paid parental leave
Comprehensive medical coverage
Emotional and mental health support programs
Compensation packages intended to align with Snap’s long-term success

Preferred Qualifications

Advanced degree (MS/PhD) in a quantitative field such as statistics, data science, computer science, economics, or operations research
Experience with causal inference libraries including CausalML, EconML, or DoWhy
Background deploying models in production and working with ML or experimentation infrastructure
Deep understanding of experimentation nuances, including intent-to-treat (ITT) versus ghost ad methodologies, and trade-offs between frequentist and Bayesian inference for decision-making under uncertainty
Experience applying causal inference in domains such as personalization and ad or marketplace dynamics

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