Sr Engineer, Machine Learning

Core Responsibilities: Design, build, and scaleML models for fraud detectionusing supervised, unsupervised, and deep learning techniques. Performexploratory data analysis (EDA)to identify anomalies, patterns, and emerging fraud behaviors. Develop and maintainend-to-end MLOps pipelineson 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, includingLLMs, 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.

Languages: Python, SQLFrameworks: Tensor Flow, PyTorch, Scikit-learn Data & Platforms: GCP, Vertex AI, PySpark, Big Query, Hadoop, HiveMLOps & Automation: MLflow, Airflow, CI/CD frameworks Collaboration: Git Hub, 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 indata science, ML engineering, or applied machine learningwith a proven track record of developing and deploying machine learning models. Proven ability to build, scale, and deployproduction ML modelsfrom experimentation to production. Strong experience withMLOps and pipeline automationusing cloud platforms (GCP / Vertex AI preferred).Proficiency in data cleaning, preprocessing, and augmentation techniques to ensure high‑quality training data Experience infraud detection, anomaly detection, or risk modelingpreferred but not required. Excellent programming and collaboration skills; able to bridge the gap between data science, engineering, and business. Familiarity with deep learning architectures like CNNs, GANs, and transformers. Expertise in tuning hyperparameters (e.g., learning rate, batch size) to optimize model performance. Evaluate model performance using metrics such as accuracy, precision, recall, and F1 score. Conduct error analysis and optimize models accordingly 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. Strong team player with ability to collaborate effectively across geographies/time zones. This position will operate as aHybrid/Flex for Your Daywork arrangement based on Target’s needs. A Hybrid/Flex for Your Day work arrangement means the team member’s core role will need to be performed bothonsite at the Target HQ MNlocation the role is assigned to and virtually, depending upon what your role, team and tasks require for that day. Work duties cannot be performed outside of the country of the primary work location, unless otherwise prescribed by Target. Click here if you are curious to learn more about Minnesota.