Benjamin schnoor
@benjaminschnoor
Machine Learning Engineer skilled in AI model development and data analysis.
United StatesWhat I'm looking for
As a Machine Learning Engineer at QBE Insurance, I have honed my skills in developing AI models, backend tools, and data pipelines that empower data scientists, actuaries, and IT teams. My journey began with a strong foundation in data science and economics, which I further developed during my internship at QBE, where I created predictive models that significantly improved fraud detection.
One of my notable achievements includes the development of a PDF data extractor using OCR methods combined with OpenAI GPT-4o, which streamlined document processing for actuaries, saving them valuable time. I take pride in leading my team to establish CI/CD pipelines that have resulted in a 100% deployment success rate. My commitment to best practices and effective communication has been instrumental in collaborating with diverse teams across the organization.
Experience
Work history, roles, and key accomplishments
Machine Learning Engineer
QBE Insurance
Jan 2024 - Present (1 year 7 months)
Developed AI models, backend tools, and data pipelines using Python for various teams. Created foundational data science and MLE tools, including a PDF data extractor using OCR and OpenAI GPT-4o, saving actuaries significant time.
Data Science Intern
QBE Insurance
May 2023 - Present (2 years 3 months)
Created predictive models to leverage business decisions, specifically developing insightful fraud detection models that improved in-house fraud detection. Presented the final fraud detection model using key metrics to non-data science teams.
Education
Degrees, certifications, and relevant coursework
UWUniversity of Wisconsin-Madison
Bachelor of Science, Data Science and Economics
2020 - 2024
Gained hands-on experience with scalable data structures, distributed computing, and parallel processing using Python. Developed efficient algorithms for handling large datasets with tools like Hadoop and Spark. Analyzed spatial data in R using supervised and unsupervised methods to segment a city into regions and governing zones, and optimize subway, bike and pedestrian routes. Learned foundation
Tech stack
Software and tools used professionally
Availability
Location
United StatesAuthorized to work in
Job categories
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