Sr Engineer - Machine Learning

Sr Engineer - Machine Learning page is loaded## Sr Engineer - Machine Learninglocations:
7000 Target Pkwy N,NCD-0375 Brooklyn Park,MN 55445time type:
Full time posted on:
Posted Todayjob requisition :
RThe pay range is $95,000.00 - $Pay is based on several factors which vary based on position. These include labor markets and in some instances may include education, work experience and certifications. In addition to your pay, Target cares about and invests in you as a team member, so that you can take care of yourself and your family. Target offers eligible team members and their dependents comprehensive health benefits and programs, which may include medical, vision, dental, life insurance and more, to help you and your family take care of your whole selves.

Other benefits for eligible team members include 401(k), employee discount, short term disability, long term disability, paid sick leave, paid national holidays, and paid vacation.

Find competitive benefits from financial and education to well-being and beyond at .Working at Target means helping all families discover the joy of everyday life. We bring that vision to life through our values and culture. .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:
** Tensor Flow, PyTorch, Scikit-learn
* ** Data & Platforms:
** GCP, Vertex AI, PySpark, Big Query, Hadoop, Hive
* ** MLOps & 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 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…