Senior Manager – Machine Learning

Kafene

Senior Manager of Machine Learning Engineering at Kafene focused on ML models for credit risk decisions. Collaborating with cross-functional teams to innovate and improve models.

Posted 4/23/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $95,000 - $140,000 per yearWebsite

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

Feature Engineering: Analyze internal and external datasets to surface trends and build high-signal features — including Debt-to-Income (DTI), Payment-to-Income (PTI), payment behavior patterns, and account balance signals — that improve the accuracy and predictive power of our credit models.
Model Development: Design and develop strategic ML models end-to-end, including approval amount sensitivity models that optimize credit line assignment strategies and drive measurable business outcomes.
Data Preparation: Manipulate, clean, and transform structured and unstructured data to ensure quality and readiness for modeling. Uphold rigorous standards for data integrity throughout the pipeline.
Vendor Evaluation: Partner with external data vendors to assess third-party data products and scoring solutions. Lead cost-benefit analyses to guide partnership decisions and data integration strategies.
Research & Innovation: Stay at the forefront of machine learning and feature engineering advances. Continuously incorporate new techniques to improve model performance and push the quality of our credit risk systems forward.
Model Implementation & Validation: Collaborate with technology and engineering teams to implement and validate models with precision. Identify opportunities to improve the speed, accuracy, and repeatability of model deployment.
Monitoring & Maintenance: Proactively track model performance in production. Lead calibration and redevelopment efforts to keep models accurate, reliable, and aligned with evolving business conditions.
Compliance & Governance: Ensure all models meet regulatory standards and data vendor usage policies. Maintain thorough documentation of development processes, methodologies, and validation procedures. Support internal and external audit processes as needed.
Cross-Functional Partnership: Work closely with risk management, engineering, finance, and sales to translate business challenges into scalable, data-driven solutions — and ensure seamless integration of models into the broader risk management framework.

Requirements

What you’ll need

Education: Master's or PhD in a quantitative discipline (Data Science, Statistics, Mathematics, Computer Science, Engineering, or related field) preferred
Experience: 5+ years of hands-on experience as a Data Scientist or Machine Learning Engineer, with a strong track record of building and deploying models in credit risk, fraud detection, or financial analytics
Advanced Python proficiency with demonstrated experience building production-grade ML models
Strong SQL skills for data querying and manipulation
Deep knowledge of ML algorithms including gradient boosting, ensemble methods, regression models, decision trees, and AutoML frameworks
Prior experience in lending, fintech, or financial services is highly preferred
Familiarity with model risk governance frameworks and experience working with validation teams is a plus
Able to translate complex technical concepts clearly for both technical peers and executive stakeholders.

Benefits

Comp & perks

Healthcare: We prioritize your well-being by covering 80% of medical, dental, and vision insurance costs, including coverage for your spouse, children, and other dependents.
Retirement Benefits: Begin planning for your future from day one with our 401k plan.
Paid Time Off: We understand the importance of work-life balance. That's why we offer flexible paid time off days starting from day one of your employment.

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Hard Skills & Tools

feature engineeringmachine learningdata preparationmodel developmentPythonSQLML algorithmsgradient boostingensemble methodsAutoML

Soft Skills

cross-functional partnershipcommunicationcollaborationproblem-solvinginnovation