Rooted in Data, Structured by Design

M.S. in Data Analytics and Policy

Johns Hopkins University
Focused on applied data science, statistical modeling, and policy analysis, this program sharpened my ability to bridge technical insights with actionable strategies. My coursework included machine learning, advanced statistical inference, data visualization, and policy evaluation—grounded in real-world government and nonprofit use cases. I also contributed to a federal data science research collaboration during this time, developing predictive modeling techniques and improving data quality pipelines.

B.S. in Marketing

Indiana University of Pennsylvania
This degree grounded me in business fundamentals while emphasizing data-driven marketing strategy. Courses in consumer behavior, brand management, and business communication introduced me to the power of analytics in shaping strategic direction. I developed foundational research skills, which I would later apply to more advanced technical work.

B.S. in Management Information Systems

Indiana University of Pennsylvania
As a dual-degree student, I also earned a second bachelor’s in MIS, where I focused on systems analysis, relational database design, and business intelligence. This combination of business and technical training gave me an early start on integrating data architecture with organizational decision-making.

Data Analyst – Law Firm

At this early-stage firm, I built Tableau dashboards that visualized case outcomes, court timelines, and client performance metrics. I queried case data using SQL, cleaned and standardized incoming datasets, and created automated reporting pipelines that saved the team hours of manual labor weekly. My work directly supported legal strategy formulation, with analytics that contributed to an 18% revenue boost in just six months. I also helped identify inefficiencies in internal data management and implemented a simplified intake structure to improve reporting accuracy.

Senior Data Analyst – Johns Hopkins Health System

At Johns Hopkins, I led efforts to scale enterprise-wide reporting and data infrastructure. I managed over 200 ETL jobs across staging and production environments, reduced latency through data pipeline optimization, and automated dashboard refreshes for critical operational KPIs. Working with datasets of over 220 million rows, I developed SQL-based workflows and integrated Python models for forecasting and classification. Beyond the technical stack, I focused on documentation and onboarding—standardizing data dictionaries, training guides, and process protocols that accelerated team onboarding and cross-functional collaboration.

Data Analyst – JDSAT (Current)

As a member of JDSAT’s hybrid data science team, I specialize in data visualization and analytics for federal clients. I work closely with engineers and stakeholders to translate complex requirements into clear, actionable dashboards using Tableau and Python-based pipelines. I also build backend SQL processes that validate and transform raw data into clean, reliable visual outputs. In addition to development, I collaborate with UI/UX designers to ensure tools meet accessibility and performance standards. This role balances both front-end interface design and backend analytics engineering—providing a 360-degree view of operational decision support.

Senior Principal Data Science Software Engineer – Northrop Grumman (Present)

Now based in Maryland, I lead the development of next-generation data engineering infrastructure for Northrop Grumman’s Microelectronics Design and Applications team. My work integrates data science with high-performance computing—specifically, designing scalable systems for analyzing data from quantum and superconducting architectures. I architect and implement robust pipelines, optimize SQL queries for high-throughput applications, and support microservices-based integration for cross-platform data use. I also mentor junior engineers and contribute to long-term architectural decisions around CI/CD practices, code review pipelines, and agile product delivery. This role is fully onsite and requires close coordination with national security stakeholders in a highly secure environment.

AI-Driven Crystal Structure Prediction – National Institute of Standards and Technology (NIST)

My work with NIST focused on one of material science’s most enduring challenges: predicting the 3D crystal structure of materials from X-ray diffraction (XRD) data. Inspired by AlphaFold’s revolution in protein folding, our aim was to build a machine learning model capable of similar breakthroughs in solid-state chemistry. I contributed to the development of a robust synthetic data pipeline using the PyXtal library, which generated thousands of valid crystal structures across all 230 space groups. Unlike traditional datasets limited by structural bias, our approach emphasized symmetry diversity and distributional balance, which are essential for model generalization.

One of the key technical challenges involved validating space group symmetry using spglib and engineering an intelligent retry system that eliminated infinite loops during structure generation. I also designed atomic composition sampling strategies that respected Wyckoff position constraints—ensuring realistic, stable configurations. Each structure was paired with a standardized, 500-point XRD pattern suitable for deep learning. This project not only laid the groundwork for transformer-based structure classification models but also demonstrated how applied data science can accelerate materials discovery. The tools and techniques I developed were integrated into a broader pipeline for training and evaluating AI-driven crystallography systems.

Consulting – Pacific Beach Coalition

As a data consultant supporting the Pacific Beach Coalition’s strategic growth, I’ve been helping them evaluate and plan their next fundraising phase. The organization has operated for over 50 years, engaging communities in coastal cleanup efforts, but is now looking to formalize leadership, expand its impact, and raise up to $2M over five years. My role includes identifying peer organizations in California’s Bay Area through GuideStar, analyzing their funding sources, and reverse-engineering successful grant and donor strategies.

We’re developing a three-pronged fundraising model targeting government, corporate, and individual contributions. I’m currently structuring a database that classifies nonprofits by program similarity, location, funding tier ($100K–$2M), and grant sources. From this, we’ll advise the PBC on where to focus their grant applications and what profile of fundraising director to hire (e.g., someone with corporate sponsorship experience vs. government grant specialization). The ultimate goal is to help them tell a compelling, data-backed story to attract multi-source funding and scale their environmental programming.