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Data Engineer wanting a transition to Machine Learning Engineering or Data Science


Amazon TextractAWSComputer VisionData analysisData pipelinesData QADeep LearningDockerFastAPIHuggingFaceKerasMachine LearningML appsNLPNumPyPythonPyTorchSageMakerScikit-learnSQLStreamlitTensorFlowTransformers


Hi there,My name is Adam Villarreal, I am currently a Data Engineer for a small AI team within Genentech building AI tools to help clinicians better quantify their results in clinical studies. However, unfortunately I was told that the company is not going to renew my contract along with hundreds, especially a handful of my colleagues. So I am here in the job search again with a renewed interest of finding work that interests me.My educational background is studying a B.S. in Data Science from the University of San Francisco from 2017 to 2021, with also finishing the top of my class receiving the Data Institute Award for Excellence in Data Science by Math and Statistics Department. While studying I took many other online MOOCs in Machine Learning/Deep Learning, where I supplemented on-demand skills in AI not provided by my university at an undergrad level. I am very proud of myself for this time in my life, which I hope I can return to my desires of working more in ML modeling as I did while a student.My role at Genentech as a Data Engineer can be best described in my opinion as a research assistant to an AI scientist specializing in Ophthalmology, where I did whatever was necessary to help propel the research by either creating software tools to assist their ML experiments and outreach, or by actually performing internal ML experiments to guide their research. To describe this a little further, some of the tools I built where building an OCR tool using AWS Textract to digitize a big catalogue of Visual Field Reports in PDFs, which is necessary to analyze a patient's disease progression. This tool lead to one conference poster presentation lead by myself (AGS 2023), and one published paper in TVST ARVO named "Repeatability of a Virtual Reality Headset Perimeter in Glaucoma and Ocular Hypertensive Patients". Another tool I am very proud of is creating a deployed ML-app that hosts 16 models built with AWS S3, Docker, FastAPI, and Streamlit. The app was used throughout the Ophthalmology community within Genentech to share our results or have others grow interest in our research. I also performed many internal ML experiments (Computer Vision Modeling) that guided internal research endeavors. One ML task I performed was fine-tuning a model to our internal clinical study dataset, that was later used in a Data QA Pipeline to clean mislabelled images using ML-Assisted Labels.I feel that my skills are very well-rounded as a junior in my career, but I hope to grow more in actually working on training actively deployed ML algorithms to see how ML/AI is affecting directly in products now rather than just academic endeavors. So I am truly open to anything, and would love to chat if interested! :)Thank you,Adam


San Diego, California, US Flag of

Last updated about 3 weeks ago