Sr Engineer, Machine Learning
Target
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Greater Minneapolis-St. Paul Area
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Mid-Senior level
Posted March 26, 2026
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Responsibilities
Commitments
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.
- Tech Stack & Tools
- Languages: Python, SQL
- Frameworks: TensorFlow, PyTorch, Scikit-learn
- MLOps & Automation: MLflow, Airflow, CI/CD frameworks
- Collaboration: GitHub, JIRA, cross-functional partnerships with Engineering, Data Platform, and Fraud Investigations Experience & Qualifications
Commitments
Strong team player with ability to collaborate effectively across geographies/time zones.
This position will operate as a Hybrid/Flex for Your Day work 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 both onsite at the Target HQ MN location 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.
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Requirements
- 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
- 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
- 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.
Preferred Skills
- MLOps & Automation: MLflow, Airflow, CI/CD frameworks
- Strong experience with MLOps and pipeline automation using cloud platforms (GCP / Vertex AI preferred).
- 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.
- Familiarity with deep learning architectures like CNNs, GANs, and transformers.
- Expertise in tuning hyperparameters (e.g., learning rate, batch size) to optimize model performance.
- Conduct error analysis and optimize models accordingly
Education
- (Not required) – Advanced degree (Master’s or PhD) in Computer Science, Data Science, Statistics, Mathematics, or a related field
About us: Working at Target means helping all families discover the joy of everyday life. We bring that vision to life through our values and culture. Learn more about Target here. 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. You’ll collaborate closely with engineering, data, and product partners across Target to deliver ML solutions that proactively detect, prevent, and adapt to emerging fraud patterns across stores and digital platforms. Core 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. 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.
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 a Hybrid/Flex for Your Day work 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 both onsite at the Target HQ MN location 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.
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.
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 a Hybrid/Flex for Your Day work 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 both onsite at the Target HQ MN location 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.