Rain

6 months ago

Machine Learning Engineer - Fraud Risk

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Build and scale production ML systems for fraud detection, anomaly detection, and behavioral analysis. Own end-to-end ML pipelines from data ingestion and feature engineering through training, deployment, and continuous monitoring. Design low-latency real-time decision systems and maintain ML infrastructure with versioning, retraining, and safe releases. Drive monitoring/alerting and experiments for explainability, drift detection, and adversarial robustness, partnering with data scientists, compliance, and platform teams to meet SLAs.

Location: New York

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Architect and build scalable ML systems for fraud detection, anomaly detection, and behavioral analysis.
•Develop and maintain end-to-end ML pipelines including data ingestion, feature engineering, model training, deployment, and continuous monitoring.
•Design and implement low-latency real-time decision systems integrating with transaction or behavioral data streams.
•Own ML infrastructure including model versioning, automated retraining, and safe deployment strategies (e.g., shadow, rollback).
•Build monitoring and alerting for model performance, latency, data quality, and drift, and lead experimentation for explainability, drift detection, and adversarial robustness.

Pay and Benefits

Salary: USD 50,000 - 999,000 annually

Equity and Bonus:Equity

Perks:Paid LeaveHealth InsuranceDentalVision401k

Key Requirements

•5+ years building ML systems in production, including 2+ years in fraud, risk, or anomaly detection domains.
•Advanced proficiency in Python and ML frameworks such as PyTorch, TensorFlow, and scikit-learn.
•Strong understanding of supervised/unsupervised learning, anomaly detection, and statistical modeling.
•Proven track record designing and maintaining ML models at scale.
•Experience developing and productionalizing predictive real-time and offline fraud detection models using supervised and unsupervised ML techniques.

Experience:5+ years

Education:Bachelor's in Computer Science, Engineering, Statistics, Applied Math, or related technical field

Skills:CollaborationAbility to work autonomouslyManage ambiguityExperimentationCross-functional communication

Tech Stack:PythonPyTorchTensorFlowScikit-learn

Company Brief

Rain

Website

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Rain

6 months ago

Machine Learning Engineer - Fraud Risk

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Location: New York

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Architect and build scalable ML systems for fraud detection, anomaly detection, and behavioral analysis.
•Develop and maintain end-to-end ML pipelines including data ingestion, feature engineering, model training, deployment, and continuous monitoring.
•Design and implement low-latency real-time decision systems integrating with transaction or behavioral data streams.
•Own ML infrastructure including model versioning, automated retraining, and safe deployment strategies (e.g., shadow, rollback).
•Build monitoring and alerting for model performance, latency, data quality, and drift, and lead experimentation for explainability, drift detection, and adversarial robustness.

Pay and Benefits

Salary: USD 50,000 - 999,000 annually

Equity and Bonus:Equity

Perks:Paid LeaveHealth InsuranceDentalVision401k

Key Requirements

•5+ years building ML systems in production, including 2+ years in fraud, risk, or anomaly detection domains.
•Advanced proficiency in Python and ML frameworks such as PyTorch, TensorFlow, and scikit-learn.
•Strong understanding of supervised/unsupervised learning, anomaly detection, and statistical modeling.
•Proven track record designing and maintaining ML models at scale.
•Experience developing and productionalizing predictive real-time and offline fraud detection models using supervised and unsupervised ML techniques.

Experience:5+ years

Education:Bachelor's in Computer Science, Engineering, Statistics, Applied Math, or related technical field

Skills:CollaborationAbility to work autonomouslyManage ambiguityExperimentationCross-functional communication

Tech Stack:PythonPyTorchTensorFlowScikit-learn

Company Brief

Rain

Website