Overview

Company: Target

Location: Minneapolis, MN (On-site, Remote)

Salary: $95K - $171K/yr

Type: Full-time

Benefits: Medical, Dental, Vision, Life, Retirement, PTO

Responsibilities

The Fraud Detection and Prevention Data Science team builds scalable, intelligent systems that safeguard Target's guests and digital channels from fraud and abuse. As a Senior Engineer, you will own the end-to-end lifecycle of machine learning solutions - from data exploration and feature engineering to model development, deployment, and continuous improvement through MLOps.

• Design, build, and scale ML models for fraud detection using supervised, unsupervised, and deep learning techniques.
• Perform exploratory data analysis (EDA) to identify anomalies, patterns, and emerging fraud behaviors.
• Develop and maintain end-to-end MLOps pipelines on Vertex AI and GCP - including training, evaluation, deployment, and monitoring.
• Partner with cross-functional teams - Engineering, Data Engineering, Investigations, and Product - to operationalize fraud models and translate insights into prevention strategies.
• Research and prototype new detection techniques, including LLMs, anomaly detection, and behavioral modeling.
• Lead technical design reviews, mentor junior data scientists/engineers, and uphold best practices through code reviews and technical sessions.
• Maintain strong documentation and model governance, ensuring reliability, reproducibility, and scalability across the ML platform.

Tech Stack & Tools

• Languages: Python, SQL
• Frameworks: TensorFlow, PyTorch, Scikit-learn
• Data & Platforms: GCP, Vertex AI, PySpark, BigQuery, Hadoop, Hive
• MLOps & Automation: MLflow, Airflow, CI/CD frameworks
• Collaboration: GitHub, JIRA, cross-functional partnerships with Engineering, Data Platform, and Fraud Investigations

Experience & Qualifications

• Advanced degree (Master's or PhD) in Computer Science, Data Science, Statistics, Mathematics, or a related field
• 5-8 years of hands-on experience in data science, ML engineering, or applied machine learning with a proven track record of developing and deploying machine learning models.
• Proven ability to build, scale, and deploy production ML models from experimentation to production.
• Strong experience with MLOps and pipeline automation using cloud platforms (GCP / Vertex AI preferred).
• Proficiency in data cleaning, preprocessing, and augmentation techniques to ensure high-quality training data
• Experience in fraud detection, anomaly detection, or risk modeling preferred but not required.
• Excellent programming and collaboration skills; able to bridge the gap between data science, engineering, and business.