Sr Engineer - Machine Learning
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Minneapolis, MN
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Not Applicable
Posted April 17, 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
- Collaboration: GitHub, JIRA, cross-functional partnerships with Engineering, Data Platform, and Fraud Investigations
Commitments
Location: Minneapolis, MN (On-site, Remote)
Type: Full-time
Not Met Priorities
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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
- 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.
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.
Education
- (Not required) – Advanced degree (Master's or PhD) in Computer Science, Data Science, Statistics, Mathematics, or a related field
- (Not required) – 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.
Job Description
Sr Engineer - Machine Learning
Company: Target
Location: Minneapolis, MN (On-site, Remote)
Salary: $95K - $171K/yr
Type: Full-time
Benefits: Medical, Dental, Vision, Life, Retirement, PTO
Job Description:
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.
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.
Sr Engineer - Machine Learning
Company: Target
Location: Minneapolis, MN (On-site, Remote)
Salary: $95K - $171K/yr
Type: Full-time
Benefits: Medical, Dental, Vision, Life, Retirement, PTO
Job Description:
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.
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.