Data Analyst Data Scientist Machine Learning Engineer
Learn more about me.I am a data scientist drawn to using data science to help further technological advancements while utilizing past work experience to establish a unique perspective when assessing data-driven solutions. Before becoming a data scientist, I worked in the scientific research industry.
Emotion Predictor using both facial images and audio recordings. Constructed multiple deep learning convolutional neural network trained on audio and images predicting both emotion and sex. Provided a real-time prediction Dash Application hosted on AWS that takes in both live images and audio.
Analyzed customer support inquiries on Twitter and applied unsupervised NLP concepts to generate topics related to customer support inquiries. Developed predictive models to predict company’s response times to customer’s inquiry tweets using generated topics.
Analyzed and visualized application statistics from a Google Play Store dataset, ran statistical hypothesis testing to depict application behavior over various levels of success. Discovered success levels have no direct effect on the distribution of ratings an application receives.
September 2020 - present
Coached Data Science Immersive students through technical assignments. Prepare and deliver lectures on python programming, machine learning, and neural network topics. Provided students actionable feedback on code readability, data analysis, and data visualization during capstone projects. Evaluated student assessments, case studies, and capstone presentations.
January 2018 - January 2019
Managed and executed research projects focusing on protein-to-protein interaction in Ebola virus. Collaborated closely with postdoctoral associates to further aid and current projects.
October 2016 - December 2017
Led research projects focusing on vector-borne diseases to examine phylogeographical relationships using statistical analysis. Collaborated closely with graduate students, postdoctoral associates, and faculty. Mentored undergraduate students in conducting short term projects and learning introductory laboratory skills.
March 2020 - June 2020
A 13-week immersive bootcamp with 700+ hours of coding, weekly Case Studies, and three capstone projects. Python-based curriculum focused on machine learning and best practices in statistical analysis, including frequentist and Bayesian methods. Utilizes regression, classification, and clustering to model real-world structured and unstructured data.
September 2015 - June 2018
Bachelor of Science in Global Disease Biology.
NumPy, Pandas, Matplotlib, Seaborn, OpenCV, Dash, Flask, Keras, Tensorflow, SciPy, Scikit-learn, SpaCy, AWS (EC2), Unix.