About

I'm a ML Engineer and Data Scientist with a 4.0 GPA in a M.S. in Data Science, specializing in applied ML and generative AI. My background encompasses scientific research, education, and early-stage tech. I build systems that turn ambiguous problems into models and pipelines that hold up under scrutiny.

I Help Decision Makers

How I Work

I’ve worked across research, education, early-stage tech, international higher education, and environmental-sector consulting, where problems are rarely well-defined and decisions need to be made with imperfect, sometimes messy data.

Across these experiences, I’ve developed a consistent approach:

clarify the decision before building the model or analysis
define metrics and evaluation criteria that reflect the real outcome, not just what’s easy to measure
build and test models, pipelines, or systems against that bar
translate findings into actions that teams can execute

I’m most effective when working closely with directors, program leads, and organizational leadership to align on what matters and move forward with confidence.

Core Capabilities

Intuitive solutions for better results

My core work is applied machine learning and generative AI: LLM systems with retrieval and real evaluation harnesses, clustering and classification models with fairness and accuracy checks built in, and computer vision pipelines that hold up outside a notebook. I pair that with the data engineering and BI skills to get a model into a system someone actually uses: relational schemas, SQL pipelines, and dashboards. I optimize for whether the system actually does what it claims.

What differentiates my work is the combination of technical depth and cross-functional fluency. I’ve worked in environments where I needed to define the problem, design the model and the analysis, and communicate the outcome clearly to stakeholders with different priorities. This allows me to bridge the gap between data and decision-making, especially in ambiguous or resource-constrained contexts.