Machine Learning Engineer II (ROA + LTV ML) at Affirm | United States (Remote)
Company/Recruiter: Affirm
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Opportunity type
Job
Location
United States of America
Work arrangement
Remote
Compensation
Not disclosed
Closing date
Oct 25, 2027
Employer
Affirm
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Role shape: Responsibility-led role with a detailed skills profile.
Evidence to prepare: Connect your CV or portfolio to Proven experience in machine learning engineering.; Strong understanding of statistical modeling and data science principles.. Use one concrete example that shows you can Design, develop, and implement machine learning models for ROA and LTV..
Check before applying: Confirm that United States of America and the stated work arrangement suit you, check the closing date of Oct 25, 2027.
- Apply before Oct 25, 2027.
- The application route is available to verified Talent accounts.
- Compensation is not disclosed in the listing; confirm it during the employer's process.
Job Overview
As a Machine Learning Engineer II, you will be instrumental in building and deploying robust, production-grade machine learning models focused on revenue optimization and customer lifetime value prediction. You will collaborate with a talented team of engineers and data scientists to drive innovation and deliver tangible business impact.Key Responsibilities
- Design, develop, and implement machine learning models for ROA and LTV.
- Collaborate with cross-functional teams to integrate ML solutions into production systems.
- Evaluate and iterate on model performance to ensure optimal business outcomes.
- Contribute to the entire ML lifecycle, from data exploration to deployment and monitoring.
Requirements
- Proven experience in machine learning engineering.
- Strong understanding of statistical modeling and data science principles.
- Proficiency in relevant programming languages (e.g., Python).
- Experience with ML frameworks and libraries.
What We Offer
- Opportunity to work on impactful projects at a leading fintech company.
- Collaborative and innovative work environment.
- Professional development and growth opportunities.
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