Architect and build scalable ML systems for fraud detection, anomaly detection, and behavioral analysis
Develop and maintain end-to-end ML pipelines: data ingestion, feature engineering, model training, deployment, and continuous monitoring
Design and implement low-latency, real-time decision systems partnering with fraud risk data scientists, integrating with transaction or behavioral data streams
Own ML infrastructure, including model versioning, automated retraining, and safe deployment strategies (e.g., shadow, rollback).
Build robust monitoring and alerting for model performance, latency, data quality, and drift
Lead experimentation on model explainability, drift detection, and adversarial robustness for fraud prevention use cases
Develop tooling and processes to improve the effectiveness and speed of the ML development lifecycle
Partner with platform teams to meet strict SLAs for availability, latency, and accuracy
Collaborate closely with talented engineer, data scientist and compliance teams across Rain
Work in a fast-paced environment on a rapidly growing product suite.
Solve complex problems at the intersection of ML systems, data, and reliability

Requirements

5+ years of experience building ML systems in production; at least 2+ in fraud, risk, or anomaly detection domains
A degree in Computer Science, Engineering, Statistics, Applied Math or a related technical field
Proven track record designing and maintaining ML models at scale
Advanced proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn)
Strong understanding of supervised / unsupervised learning, anomaly detection, and statistical modeling
Ability to work autonomously, manage ambiguity, and collaborate closely with data scientists to translate analytical models into robust fraud prevention systems
Experience developing, validating, and productionalizing predictive real-time and offline fraud detection models using supervised and unsupervised ML techniques.
Experience collaborating with cross-functional teams to prioritize, scope, and deploy MLI solutions at scale

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

machine learningPythonPyTorchTensorFlowscikit-learnanomaly detectionsupervised learningunsupervised learningstatistical modelingmodel deployment

collaborationproblem-solvingautonomyambiguity managementleadershipcommunicationexperimentationmonitoringalertingprocess improvement