Ednalyn C. De Dios, a.k.a. “Dd”
(210) 236-2685


Over ten years of management experience. Four years of data science experience in various industries, including FinTech/BPO, pharmaceuticals, and tech. CompTIA A+, Project+, and Network+ certified.


Served as a senior associate data scientist in the FinTech/BPO industry, leveraging natural language processing, network graph analysis, and other techniques to extract insights from large datasets.

Developed automation solutions for a pharmaceutical company, including optical character recognition (OCR) and natural language processing (NLP) techniques to convert unstructured data into functional business documents.

Currently serving as a Machine Learning Support Engineer at Microsoft, providing technical support, and troubleshooting for machine learning models and systems.

Created agency’s domestic violence ‘Crisis to Confidence’ program as Family Violence Unit Director, and increased productivity of staff members and volunteers by 50% as Office of Project Management and IT Director at Aware Central Texas.


Natural Language Processing – Spacy – NLTK – GSDMM – LDA – Network Graph Analysis – Data Wrangling – ETL – Data Crunching – EDA – Applied Statistics – Machine Learning –Distributed Data – Data Storytelling – Git – Jupyter Notebooks – Anaconda – Python – SQL – Spark – Tableau – Pandas – Numpy – Matplotlib – Seaborn – Scikit Learn – HTML – CSS – JavaScript – Visual Basic – Bootstrap – PHP – Power BI – OCR – Computer Vision – Azure Machine Learning Studio


InsightsGlobal – Merck & Microsoft

Responsible for providing technical support and troubleshooting for machine learning models and systems. This involves model monitoring and troubleshooting, technical support, documentation and training, performance optimization, collaboration with data scientists and developers, and research and development.

Developed automation solutions for a pharmaceutical company. Performed optical character recognition (OCR) natural language processing techniques (NLP) to convert large unstructured datasets into functional business documents. Worked in Python, AWS, and UiPath.


Worked with large structured and unstructured data sets, cleaning/scrubbing data sets, build/deploy models. Scoped innovative solutions. Worked with stakeholders on applying and delivering AI at scale. Conducts exploratory data analysis using NLP techniques. Created end-to-end data pipelines in AWS using Lambda, API Gateway, etc. Delivered actionable insights to stakeholders.

Aware Central Texas

Increased productivity of staff members and volunteers while serving as Chief Information Director. Established agency’s domestic violence ‘Crisis to Confidence’ program as the Family Violence Unit Director in a child abuse prevention agency.


AI-Powered Text Generation App

Used GPT-2, Hugging Face Transformers, and PyTorch to develop a web app that takes a user’s prompt as input and generate relevant text as output. Deployed the app using Flask and NGINX. Developed in Python.

NLP Engine

Developed and deployed a natural language processing engine that takes in unstructured data and generates n-grams, sentiment analysis, and named entities.

Work-From-Home Monitoring

Developed and deployed ETL pipelines for extracting client tool data from various sources.

Top 40 Playlist – NLP App

Developed a Windows GUI Application that employs n-gram ranking and named-entity recognition on a given dataset.

Predicting SSO’s

Winning entry in CivTechSA Datathon Competition. Predicted sewage overflows using regression and classification models.

Predicting Reassault

Capstone project; predicted probability of reassault in domestic violence cases. Took historical data, cleaned it, prepped it, and trained it on several models including Logistic Regression with Cross Validation, Decision Tree, and Random Forest. Used XGBoost and other methodologies for feature extraction including chi-square testing. Deployed on an Ubuntu server running nginx and used the Flask framework to run the Python code.

Alumni Employability

Used web scraping, TF-IDF, and sentiment analysis, and random forest classifier to analyze and predict an alumnus’ hiring chances based on the quote displayed on his or her profile on Codeup’s hiring portal.

NLP Analysis
Used web scraping, TF-IDF, and sentiment analysis, logistic regression to analyze the readme files of Texas Tribune’s GitHub.

Anomaly Detection
Identified anomalies using statistical methods, clustering, and DBSCAN.

Time-Series Analysis
Forecasted health status on Fitbit data.

Improved estimate of log error on Zillow data.

Classification Project
Used Logistic Regression, Decision-Trees, and Random Forest to predict customer churn.

Linear Regression Project
Made recommendations to reduce the churn rate.

Automated Tweet Generator
Developed a console application in Python which automatically makes a Twitter post.


Western Governors University, MS in Data Analytics

Western Governors University, BS in Data Management and Data Analytics


Fully-immersive, project-based 18-week Data Science career accelerator that provides students with 600+ hours of expert instruction in applied data science. Students develop expertise across the full data science pipeline (planning, acquisition, preparation, exploration, modeling, delivery), and become comfortable working with real, messy data to deliver actionable insights to diverse stakeholders.


Winner of CivTechSA Datathon 2019 – Joint-Service Commendation Medal – Navy Good Conduct Medal – Admiral’s Letter of Merit – Sailor of the Year – Sailor of the Quarter – Global War on Terrorism Service Medal – Iraqi Campaign Medal – Armed Forces Expeditionary Medal – Enlisted Surface Warfare Specialist – Enlisted Aviation Warfare Specialist