Skills & Stack
GenAI & Agentic
AWS Bedrock / Claude
Claude Code
AI-Augmented Development (Vibe Coding)
LLM Cost Optimization
Prompt Engineering
LLM Feature Gen
Vision LLMs
Modeling & ML
XGBoost
Classification & Regression
Bayesian & Markov Models
Time-Series Forecasting
Discrete-Choice Modeling
Monte Carlo Simulation
Data Engineering
Python (Pandas / PySpark)
SQL
AWS (Glue / Redshift / Lambda)
Apache Airflow
ETL Pipelines
Data Warehousing
Feature Engineering
Geospatial Systems
Python Geospatial
ArcGIS / QGIS
Shortest Path Algorithms
NetworkX
OSMnx
OpenStreetMap
Analytics & Experiment
A/B Testing
KPI Design
R
Stakeholder Communication
Dashboarding & BI
Dashboarding
Tableau
Streamlit / Plotly
Advanced Excel
Work Experience
Data Scientist III
Choice Hotels International
- Sales Lead Scoring Platform: Built a product that scores and ranks prospective B2B customers for hotel sales teams. Cut sales lead research from an hour to minutes for teams covering 6,000 properties. Delivered as a Tableau dashboard integrating Salesforce, internal performance data, and external vendor data. Engineered multiple ML and LLM-based features that feed a single ranked score.
- Customer Lifetime Value Model: Built an XGBoost CLV model on 50M+ transaction records. Improved customer segment accuracy by 25%. Informed targeting decisions on $100M+ in system fees for marketing and seasonal planning. Built a deduplication pipeline to clean the underlying data.
- Vision-LLM Marketing Photo Compliance Pipeline: Built an AI system that scores and ranks marketing photos across 6,000 franchised hotel properties against brand and compliance rules. A contractor quoted $45K and 6,090 hours to do this manually. Delivered the same coverage in a single automated run for under $1K, a 97% cost reduction. Flags violations including bathroom hero images, parking-lot-heavy exteriors, competitor logos, and guests without releases. Routed full-portfolio runs through Bedrock Batch Inference and downsampled images to reduce per-call token cost.
- External Data Matching Pipeline: Designed a multi-stage algorithm to match external data sources against 60,000 US hotels. Replaced a single-step name-matching approach. Added fuzzy matching, geospatial matching, and LLM-based vector similarity for hard cases. Lifted match rate from 50% to 96%. Deployed on AWS Glue.
- Climate Risk & Opportunity Dashboard: Built a first-of-its-kind tool for area directors covering 6,000 properties. Replaced ad-hoc property reviews with a single, unified view. Translated raw climate, solar, EV charging, and national park data into user-friendly KPIs (high, medium, low). Layered in GenAI (Claude through AWS Bedrock) to generate actionable, property-specific summaries.
Lead Transportation Networks Data Scientist
ICF International
- Built a Python and GIS pipeline to audit GTFS data quality across FHWA's national transit inventory. Flagged misaligned road geometry, missing stops, and other structural errors. Ranked networks by severity so the worst offenders could be addressed first. Delivered findings via technical documentation and stakeholder dashboards.
- Built Python and GIS isochrones to measure walking and biking access to bus stops along the Bronx highway corridor for NYSDOT. Mapped current transit access gaps. Modeled scenarios for proposed north/south access restrictions. Quantified how the changes would affect resident access to transit.
- Powered the backbone of Virginia OIPI's public data inventory by computing statewide travel-time reliability KPIs in R. Partnered directly with stakeholders to define KPI methodology. Delivered detailed methodology reports for client review.
Transportation Systems Data Modeler
C&M Associates
- Owned the data and simulation pipeline behind long-term traffic and revenue forecasts for state DOT projects, including the I-495 toll road extension. Ran data ingestion into the company data warehouse, outlier detection, survey processing, travel-demand modeling, and calibration in Python, GIS, and Excel.
- Built revenue forecasts and sensitivity analyses for the Dulles Toll Road 30-year planning project, feeding VDOT's long-term contracting and operator decisions. Modeled toll rate scenarios and demand sensitivities in Python and advanced Excel, working directly with VDOT stakeholders.
Data Science Graduate Research Assistant
George Mason University
- Developed a VDOT Tableau dashboard to visualize and expose intersection-level traffic metrics, enabling improved operational awareness for state transportation stakeholders.
- Parsed and cleaned raw sensor text feeds into a structured, queryable database to support downstream data analysis and reporting requirements.
- Delivered an end-to-end dashboard solution by transforming raw sensor data into actionable stakeholder insights, streamlining traffic monitoring and reporting processes.
