The Fraud Detection and Prevention Data Science team at Target builds scalable, intelligent systems to protect Target's guests and digital channels from fraud and abuse.
Requirements
- Python, SQL
- TensorFlow, PyTorch, Scikit-learn
- GCP, Vertex AI, PySpark, BigQuery, Hadoop, Hive
- MLflow, Airflow, CI/CD frameworks
- GitHub, JIRA, cross-functional partnerships with Engineering, Data Platform, and Fraud Investigations
- 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.
Responsibilities
- 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.
Other
- Hybrid/Flex for Your Day work arrangement
- Work duties cannot be performed outside of the country of the primary work location, unless otherwise prescribed by Target.
- Excellent programming and collaboration skills; able to bridge the gap between data science, engineering, and business.
- Strong problem-solving skills, passion for solving interesting and relevant real-world problems using a data science approach.
- Excellent communication skills. Ability to clearly tell data driven stories through appropriate visualizations, graphs, and narratives.
