🤔MITRE
As a Principal Data Scientist at MITRE, I leverage my expertise in data science, statistics, and data management to consult with federal agencies, translating complex challenges into appropriate technical approaches. With over 10 years of experience, I have led technical teams to deliver diverse solutions across multiple FFRDCs, showcasing my skills in technical solutions and communication.
I am also passionate about driving innovation and cross-disciplinary collaboration. I have developed innovative technical solutions as an individual contributor and technical lead, integrating creativity and vision to drive success. Additionally, I have collaborated with cross-functional teams to implement cutting-edge data science solutions. My education background includes a Master of Science in Operations Research from George Mason University. I am also proficient in Python for Data Science and Computing for Data Analysis.
Epsilon
I supported multiple clients by using various analytic techniques including but not limited to Optimization, Data Mining, Natural Language Processing, and Machine Learning. These skills are applied through a combination of R, Python, SAS, and Netezza.
I served as one subject matter experts in the following areas: NLP and text analytics, optimization, and big data solutions. Typical duties include hosting “lunch and learns”, providing support on business development efforts, and producing code samples in multiple programming languages.
IBM
Worked with a team to determine the best use of IBM’s analytical skills to help Aetna improve their business. Modified a SAS multiplicative regression model to be more flexible with data and improve efficiency. Determine the important factors in improving care management efficiency for existing programs at Aetna.
Supported JIEDDO using various analytical techniques including Analytic Hierarchy Process and Regression Analysis. Created and tested a metric to help support decision making for various groups of people working with JIEDDO. Improved existing products in Excel and Access using SAS code. Created SAS Stored Processes to help streamline report generation. Improved raw data cleansing and formatting using regular expression parsing. Streamlined a process to parse XML files and create new databases from the results. Developed SAS stored processes to support business intelligence and analytics. Designed a database to enhance reporting and help determine an optimal solution to a resource allocation problem.
SAIC
As an Operations Research Analyst, I had the responsibility for taking a list of directions and being able to produce a solution with little to no guidance. This involved working with EXCEL, VBA, SAS, ARENA, and AnyLogic.
I was responsible for the team of interns. I worked with other SAIC employees to both screen and interview applicants for the Operations Research internships. I provided a list of tasks, providing feedback on work, and supervised the team of interns.
George Mason University
Worcester Polytechnic Institute
Technologies I use to build things
Swithing from JavaScript to Python for my SES forwarding and setting up for a better spam filter.
After setting up a shiny server, I watched how much it cost and was quite surprised.
My adventures in setting up a shiny server using AWS and hugo.
Using R and Dynamic Programming to simulate a baseball game.
This list of projects represents the details of my experience across my career and how I supported customers’ decision making on complex challenges.
I designed and built an end-to-end Retrieval-Augmented Generation (RAG) system to transform raw legislative text into validated, context-aware, and easily visualizable outputs.
Support the IRS by helping them provide an enterprise approach to data and AI governance as well as provide recommendations on aligning investments and activities to an entperise strategy.
Support IRS staff with understanding evolutionary algorithms and adapting them for use within their environment.
As an individual contributor, I helped assess key steps to modernize a complex data engineering and analysis process built in Excel.
Leading an accelator program to kickstart companies interested in supporting the federal government.
As the Project Lead, we supported CDC’s efforts in upskilling their staff in a variety of data science competencies from time series regression to data wrangling to communicating your results to all audiences.
Using blogdown to create and host a website on AWS
Taking a quantitative approach to identify which players are similar to each other.
Predict the next pitch based on pitcher batter histories.
This is an overview to optimization and Proc OPTMODEL in SAS.
Design and implement a model to perform real time predictions.
Using stats to determine how similar baseball players are to each other.
Translating unstructured text to interpret feelings towards a brand.
Using machine learning to predict which accounts are likely to be closed.
Supporting the warfighter with data
Simulate port operations to optimize tradeoff between speed and security.
Statistical approach to determining regional based fraud in healthcare claims.
Creating fair and equal softball teams
Optimizing allocation of resources to maximize mission success
Risk analysis combined with data to protect transit systems.
Dive into the complexities of building reliable AI models in high-stakes environments, where data integrity can mean the difference between success and unintended consequences.
A talk offered to the UMass Amherst Student Chapter of SIAM sponsored by the SIAM Visiting Lecturer series.
Technical talk for different groups of folks interested in the intersection of baseball and machine learning.
A talk offered to the Dublin Area Student Chapter of SIAM sponsored by the SIAM Visiting Lecturer series.