Data Scientist | Machine Learning Specialist | Research ScientistResults-driven Data Scientist with a strong background in Chemical Engineering and Applied Mathematics. Expertise in:- Machine Learning & AI (PyTorch, scikit-learn, XGBoost)- Statistical Analysis & Experimental Design- A/B Testing & Hypothesis Testing- SQL, Hive, and Data Pipelining- High-Performance Computing- Image Processing & Computer Vision- Natural Language Processing (NLP)- Large-scale Data Analysis- Simulation & Modeling (Monte Carlo, FEA)Passionate about leveraging data science and AI for impactful innovations, particularly in healthcare technology and environmental science. Skilled in developing sophisticated tools for analyzing high-dimensional datasets, conducting simulations for human factors evaluation, and creating data-driven solutions for complex problems.Recent accomplishments include:- Developing predictive models for wearable device fitment using feature engineering and Monte Carlo simulation- Creating high-performance computing pipelines for large-scale FEA simulations- Implementing machine learning models (CNN, Random Forest) for medical diagnostics- Designing and conducting A/B tests for mobile app development in healthcareProficient in Python (Pandas, NumPy, SciPy), SQL, Hive, MATLAB, and various data visualization tools. Experienced in both academic research and industry applications, with a track record of publishing in computational fluidics and molecular diagnostics.MS in Applied Mathematics and BS in Chemical Engineering from the University of Washington.Seeking opportunities to apply data-driven solutions to challenging problems in technology and healthcare. Open to collaborations and discussions on machine learning, experimental design, and technology's role in improving user experiences and health outcomes.
Self-Employed
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Independent Data ScientistSelf-EmployedSeattle, Wa, Us
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Data Scientist 2Meta Aug 2023 - Apr 2024Menlo Park, Ca, Us- Engineered a data pipeline synchronizing multi-instrument data for wearable device tension analysis, crucial for product configuration decisions.- Implemented Monte Carlo simulations to estimate population-wide knuckle circumference distribution, directly impacting SKU decisions and maximizing market coverage.- Developed a high-performance computing pipeline for Ansys FEA simulations, processing large datasets of 3D mesh models to inform critical engineering design choices.- Created machine learning algorithms to identify outlier data, enhancing data quality for wearable device research.- Designed and executed pilot studies with comprehensive SOPs and Python notebooks, ensuring reproducibility and knowledge transfer. -
Data ScientistUniversity Of Washington Bioengineering Jun 2022 - Aug 2023Seattle, Wa, Us• Led a project analyzing medical chat data between patients and providers. o Performing sentiment analysis, topic modeling, and other NLP techniques to identify trends over time.• Developed AI chatbot for integration into mobile-healthcare app for monitoring tuberculosis drug adherence, using prompt engineering, chain-of-thought reasoning, and a skillfully crafted system message. o Implemented embedded Pinecone vector database using LangChain for patient data and chat history.• Carried out statistical machine learning analysis for upcoming publication, including cluster analysis using K-means and appropriate statistical tests for different features.• Developed the DeDx (Data Enhanced Diagnostics) model and pipeline, combining rapid diagnostic test (RDT) results with patient data with machine learning models to significantly improve predictive outcomes for an RDT with low sensitivity. o Cross-validated Bayesian, random forest, XGBoost, SVC, and Dense NN models.• Developed a Flask tool for numerically modeling the amount of DNA bound to its complement using the NUPACK Python package inside of a Docker container, which is used for experimental design and prediction. o Used the NUPACK tool to explain confounding experimental phenomena, resulting in vastly improved design criteria for the molecular detection strategy, reducing the searchable domain by an order of magnitude. -
Research Scientist/Engineer 2University Of Washington Bioengineering Jul 2018 - Jun 2022Seattle, Wa, Us• Developed an at-home diagnostic test for TB drug adherence using computer vision integrated into a smartphone app. The app recognized the aruco marker on the diagnostic cartridge, and aligned the test reading window for the software to evaluate.o Utilized OpenCV in Python for object recognition functions, resulting in improved accuracy and useability.o Optimized the existing chemistry and discovered alternative chemical routes to enhance the performance and safety of the device.o Parametric 3D modeling of microfluidic devices and used 3D printers to produce over 10,000 units for the study.• Led a team of 6 in generating a labeled training dataset of images of RDTs for COVID-19, and went on to develop and train a CNN model to predict the concentration of the covid spike protein antigen in the sample, utilizing PyTorch.
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Research Scientist/EngineerUniversity Of Washington Bioengineering Mar 2016 - Jul 2018Seattle, Wa, Us• Developed a computational model for RDT design from first principles, and used the model to successfully design a complex lateral flow device that provides semi-quantitative information about the target concentration, leading to enhanced diagnostic decisions in low resource settings.• Published multiple articles in the fields of computational fluidics and chemical reaction modeling, image processing, and molecular diagnostics.
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Associate Research EngineerUniversity Of Washington Chemical Engineering Jun 2012 - Jan 2016• Used image recognition software with fluorescence microscopy to evaluate non-fouling abilities of prototype polymers.• Conducted hypothesis testing on properties of experimental polymer formulations, which led to the renewal of over $0.5MM in Office of Naval Research and DARPA grants.• Applied polymerization methods including free-radical polymerization, atom transfer radical polymerization, random co-polymers, block co-polymers, and surface functionalization. -
Academic Chemistry Lab FacilitatorSeattle Central College Aug 2011 - Jun 2013Seattle, Wa, Us• While facilitating general & organic chemistry lab sessions, became skilled in simply explaining complex concepts to students.• Streamlined the chemical inventory management system by implementing a spreadsheet using Excel. -
ServerCafe Flora Aug 2008 - Sep 2012- Capable of working in a fast paced environment.- Gained interpersonal relationship skills through direct customer service.- Managed multiple tasks at once while exercising short-term memory.
Daniel Leon Education Details
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University Of WashingtonChemical Engineering -
University Of WashingtonApplied Mathematics
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University Of WashingtonApplied Mathematics
Frequently Asked Questions about Daniel Leon
What company does Daniel Leon work for?
Daniel Leon works for Self-Employed
What is Daniel Leon's role at the current company?
Daniel Leon's current role is Independent Data Scientist.
What schools did Daniel Leon attend?
Daniel Leon attended University Of Washington, University Of Washington, University Of Washington.
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