Senior ML Engineer — Causal Marketing & Production Systems

Haus Analytics; San Francisco (CA); On-site; USD 200,000 - 260,000; Full time

Benefits offered by this job

Flexible PTO

Equity

Health insurance

WFH stipend

Events & Offsites

Free lunch

New parent leave

Job summary

Haus is seeking a senior ML engineering leader to drive advanced marketing planning, analysis, and optimization using optimization, ML, and causal inference. You will work with applied scientists, data scientists, data engineers, and other MLEs to deliver trustworthy, scalable ML solutions at scale.

Responsibilities include end-to-end ML development, productionizing models, code reviews, and mentoring engineers while collaborating with product and engineering teams to scale Haus’ product lines.

Qualifications

• PhD or equivalent experience in CS, engineering, math or related field.
• 10+ years industry experience building and operating production ML systems.
• Experience with exploratory data analysis, statistical modeling, hypothesis testing, and experimental design.
• Experience working with cross-functional teams (product, science, ops, etc).

Responsibilities

• Drive ML initiatives from concept to final product delivery across design, development, optimization and productionization.
• Implement probabilistic techniques into reusable statistical libraries (bootstrapping, tests, ML models).
• Build and maintain ML systems powering Haus products (cMMM).
• Review code and designs of teammates, providing constructive feedback.
• Lead with engineering and cross-functional partners to drive system development from ideation to production.
• Drive design and implementation of AI workflows for ML pipelines (model validation).
• Mentor ML engineers and raise the organization’s ML bar.

Skills

Machine Learning Engineer

Production systems

Python

Code review

Education

PhD or equivalent

Job description

Haus is seeking a senior ML engineering leader to drive advanced marketing planning, analysis, and optimization using optimization, ML, and causal inference. You will work with applied scientists, data scientists, data engineers, and other MLEs to deliver trustworthy, scalable ML solutions at scale.

Responsibilities include end-to-end ML development, productionizing models, code reviews, and mentoring engineers while collaborating with product and engineering teams to scale Haus’ product lines.