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Fraud Detection Machine Learning Jobs in Michigan

Whiterabbit.ai

Our mission at Whiterabbit.ai is to save lives and eliminate suffering through the early detection ... Learn and understand a large body of research in deep learning and machine learning * Participate ...

Tata Consultancy Services

AI Researcher

Detroit, MI · On-site

... computer vision and machine learning algorithms that power autonomous robots, humanoids, and ... Work on topics such as object detection, pose estimation, scene understanding, 3D reconstruction ...

Canopy

Senior Data Analyst

Detroit, MI · On-site +1

$96K - $132K/yr

Join Canopy, a Ford-backed company, at the forefront of engineering advanced threat detection and ... Lead the design, development, and implementation of advanced machine learning models and algorithms ...

Magna

Engineer I - Software Algorithm

Auburn Hills, MI · On-site

Strong grasp of machine learning concepts and neural network architectures (CNNs, RNNs, transformers). * Experience in image segmentation, object detection, and image data preparation/enhancement (e ...

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Fraud Detection Machine Learning information

What are some common challenges faced by professionals working in Fraud Detection Machine Learning, and how can they be addressed?

Professionals in Fraud Detection Machine Learning often face challenges such as dealing with highly imbalanced datasets, rapidly evolving fraud patterns, and the need for real-time detection. Managing data imbalance requires careful selection of evaluation metrics and specialized algorithms. Staying ahead of new fraud tactics involves continuous model retraining and close collaboration with domain experts. Additionally, integrating machine learning solutions with existing systems often requires cross-functional teamwork with IT, security, and compliance teams.

What is fraud detection using machine learning?

Fraud detection using machine learning involves leveraging algorithms and data analysis techniques to identify suspicious or fraudulent activities in various domains, such as banking, e-commerce, or insurance. These systems analyze large volumes of transaction data to detect patterns or anomalies that may indicate fraud. Machine learning models can adapt over time, improving their accuracy as they are exposed to more data. This approach helps organizations automate and enhance their ability to prevent, detect, and respond to fraudulent behavior efficiently.

What is the difference between Fraud Detection Machine Learning vs Fraud Analyst?

AspectFraud Detection Machine LearningFraud Analyst
CredentialsData science, machine learning certifications, programming skillsFinance, criminal justice degrees, analytical skills
Work EnvironmentData-driven, tech-focused, often in financial or e-commerce sectorsInvestigative, report-focused, in financial institutions or insurance companies
Employer & IndustryTech companies, banks, e-commerce platformsFinancial institutions, insurance firms, retail

Fraud Detection Machine Learning involves developing algorithms to identify fraudulent activities automatically, relying heavily on data analysis and programming. Fraud Analysts manually investigate suspicious cases and interpret data insights. While both roles aim to prevent fraud, Machine Learning specialists focus on building models, whereas Fraud Analysts focus on case investigation and decision-making.

What are the key skills and qualifications needed to thrive as a Fraud Detection Machine Learning Specialist, and why are they important?

To thrive as a Fraud Detection Machine Learning Specialist, you need strong expertise in machine learning, statistical analysis, and programming languages like Python or R, typically supported by a degree in computer science, data science, or a related field. Familiarity with tools such as TensorFlow, Scikit-learn, SQL databases, and experience with big data platforms or cloud services is highly valuable. Critical thinking, attention to detail, and effective communication are crucial soft skills for identifying complex fraud patterns and collaborating with interdisciplinary teams. These competencies are vital for developing accurate models that protect organizations from financial losses and maintain trust with customers.
What are popular job titles related to Fraud Detection Machine Learning jobs in Michigan? For Fraud Detection Machine Learning jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Fraud Detection Machine Learning jobs in Michigan look for? The top searched job categories for Fraud Detection Machine Learning jobs in Michigan are:
What cities in Michigan are hiring for Fraud Detection Machine Learning jobs? Cities in Michigan with the most Fraud Detection Machine Learning job openings:
Fraud Detection Machine Learning jobs near you
Infographic showing various Fraud Detection Machine Learning job openings in Michigan as of July 2026, with employment types broken down into 90% Full Time, 7% Part Time, and 3% Contract. Highlights an 93% Physical, 2% Hybrid, and 5% Remote job distribution.
Research Scientist Intern (2025)

Research Scientist Intern (2025)

Whiterabbit.ai

Internship

Posted 7 days ago

Job description

We are looking for a Research Scientist Intern to push the state of the art of our AI models. As a Research Scientist Intern at Whiterabbit.ai, you will:

  • Play a key role in architecting the algorithms and models that will power our products
  • Train on a dedicated high-performance compute cluster specialized for deep learning research
  • Work with doctors and healthcare professionals to identify serious problems and leverage their domain expertise to build robust solutions
  • Remain an active contributor to the research community by partnering with universities and publishing high impact papers

Who we are:

Our mission at Whiterabbit.ai is to save lives and eliminate suffering through the early detection of cancer with artificial intelligence. We collaborate closely with one of the top medical schools in the country and have exclusive access to one of the world’s largest cancer datasets with millions of images. We invent algorithms that make doctors more productive, more accurate, and more capable. We build products and services with a relentless focus on transforming the patient’s healthcare experience.

Responsibilities

  • Develop highly scalable classifiers and detectors that solve real-world problems
  • Learn and understand a large body of research in deep learning and machine learning
  • Participate in cutting-edge research for medical applications of computer vision

Must Have Experience

  • Experience with deep learning and convolutional networks
  • Strong theoretical and empirical research background
  • Fluency with a deep learning framework and Python

Nice to Have Experience

  • Contributions to research communities and efforts, such as publications at conferences like CVPR, NeurIPS, ICCV, ECCV, ICML, and ICLR
  • Large scale machine learning experience working with terabytes of data
  • Implemented custom operations/modules in a deep learning framework
  • Imagination, ambition, and curiosity

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