Driven by an unwavering commitment to effect positive societal change, I have consistently sought opportunities that promote community engagement and provide avenues to give back to the community. Transitioning from a decade-long career in education management, I've cultivated a growing passion for leveraging data to create impactful solutions as an early career data scientist. At StartOut, I gathered and analyzed both policy (LGBTQ+ legislation and fiscal) and non-policy (demographic, geographic, social) data, integrating them with indicators of high-growth entrepreneurship to train machine learning models. This enabled me to provide actionable insights to stakeholders and policy recommendations to decision-makers. This experience has honed my practical skills in extracting valuable insights from raw data to address pressing social issues.

As a career changer, my background encompasses diverse roles including management, classroom instruction, content writing, training and development, program administration, and career advising. Crafting content and conducting MCAT bootcamps has refined my written and verbal communication skills, enabling me to convey technical concepts clearly and fluently to both technical and non-technical audiences. My collaboration on machine learning models with data scientists around the world at Omdena, coupled with my prior dedication to teaching students from underrepresented backgrounds, has instilled in me cultural humility and competence while allowing me to contribute to meaningful causes.

Fueled by a passion for social good and equipped with a versatile skill set, I am committed to deploying data-driven solutions to address complex challenges within your organization and the broader field of data science.

StartOut Index

• Role: Task Lead
• Description: I contributed to the Omdena Myanmar chapter as the Tuberculosis team lead for their project to democratize access to resources for the following respiratory lung disorders: tuberculosis, lung cancer, pneumonia, and COVID. Four teams worked alongside each other in building models for each disease for 8 weeks, and within each team, we selected the best model for deployment. We had a team member who was experienced in Streamlit that developed a webapp for the model.

• Role: Task Lead
• Description: This was an offshoot of the original project above because there was not enough participation in the original group for COVID. I worked with the members in this new challenge to provide what EDA and data preprocessing I had already completed, as well as the dataset. I worked with and supported the group to ensure the goals and deadlines were met, but did not continue to finish the project.

• Role: Lead ML Engineer
• Description: We collaborated in this OmdenaLore AI challenge with the Giga team, a joint initiative between UNICEF and ITU for two months. We built several Computer Vision and Deep Learning models to detect school locations in Sudan using Satellite Imagery. We did an extensive and thorough analysis of the data and built multiple models using datasets provided by the Giga team to solve this problem.

• Technologies: Python, Numpy, Pandas, Tableau
• Status: Developed timeline feature for dashboard and proposed some visualization changes

• Technologies: Python, Numpy, Pandas, Geopandas, Tableau, SciPy, Scikit-learn, Matplotlib
• Status: Contributed Tableau visualizations for EDA and pipeline for data processing

1. Introduction to Tensorflow for Artificial Intelligence, Machine Learning, and Deep Learning: 🎖️
2. Convolutional Neural Networks in Tensorflow: 🎖️
3. Natural Language Processing in Tensorflow: 🎖️
4. Sequences, Time Series and Prediction: 🎖️

1. Neural Networks and Deep Learning: 🎖️
2. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization: 🎖️
3. tructuring Machine Learning Projects: 🎖️
4. Convolutional Neural Networks: 🎖️
5. Sequence Models: 🎖️

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