Anti-Fraud Analyst

Golden Apple; United States; Remote; USD 80,000 - 110,000; Full time

Job summary

Golden Apple seeks an Anti-Fraud Analyst to enhance fraud detection using ML models and Python-based tooling. You will design scoring systems, train models, and validate them against evolving fraud patterns.

Focus on feature engineering, traffic analysis, and robust model validation across transactional data and session traffic, using scikit-learn, XGBoost, and TensorFlow.

Qualifications

• Strong Python skills for ML model development.
• Experience with fraud detection on transactional data.
• Ability to analyze large volumes of data and extract meaningful features.

Responsibilities

• Develop ML models for fraud detection in real-time and batch scenarios.
• Engineer features from transaction and session traffic data.
• Validate models, monitor performance, and iterate to reduce fraud risk.

Skills

Python

Machine Learning

Feature Engineering

Fraud Detection

Traffic Analysis

Model Evaluation

Tools

scikit-learn

XGBoost

TensorFlow

Job description

Golden Apple seeks an Anti‑Fraud Analyst to enhance fraud detection using ML models. Candidates should have strong Python and ML skills, particularly in model development and traffic analysis.

In this role, you will focus on developing and implementing machine learning models specifically designed for fraud detection within transactional and session traffic. Your responsibilities will include feature engineering, traffic analysis, and model validation, ensuring robust performance against fraud patterns.

You will work with a variety of ML frameworks, including scikit‑learn, XGBoost, and TensorFlow, to design scoring systems and integrate them with rule‐based engines. Success in this position will depend on your ability to analyze large volumes of unstructured data and effectively monitor model performance.