Causal ML Engineer: Uplift & A/B Testing, Equity

Snap; New York (NY); Hybrid; USD 199,000 - 313,000; Full time

Benefits offered by this job

Paid parental leave

Comprehensive medical coverage

Emotional and mental health support programs

Job summary

Snap is seeking a Machine Learning Engineer to design and build models that optimize decision-making and drive value for users and advertisers. This role involves developing causal machine learning solutions and interpreting A/B tests while collaborating with cross-functional teams.

The ideal candidate has extensive experience in machine learning, strong Python skills, and a background in causal inference or experimentation. Snap offers a competitive salary range based on location, skills, and qualifications.

Qualifications

• 5+ years of post-Bachelor’s experience in machine learning, with hands-on experience in causal inference.
• Experience designing and analyzing online experiments (A/B tests).
• Advanced degree (MS/PhD) in a quantitative field preferred.

Responsibilities

• Design and build models for causal impact and decision-making.
• Develop causal machine learning solutions using observational and experimental data.
• Conduct code reviews and maintain high engineering standards.

Skills

Causal inference

Python proficiency

Data science

Statistical thinking

Communication skills

Education

Bachelor’s degree in computer science, statistics, economics, or a related technical field

Tools

CausalML

pandas

NumPy

scikit-learn

Job description

Snap is seeking a Machine Learning Engineer to design and build models that optimize decision-making and drive value for users and advertisers. This role involves developing causal machine learning solutions and interpreting A/B tests while collaborating with cross-functional teams.

The ideal candidate has extensive experience in machine learning, strong Python skills, and a background in causal inference or experimentation. Snap offers a competitive salary range based on location, skills, and qualifications.