Nellie Cordova

About me

Careful analysis. Clear conclusions.

Hi—I'm Nellie.

Applied ML and data analysis with an engineering mindset.

I'm an applied machine learning practitioner with a strong quantitative and software engineering foundation, currently completing an M.S. in Computer Science (Machine Learning). I focus on building ML models and systems that are useful, measurable, and grounded in real-world constraints.

My recent work centers on applied ML projects that combine vision, NLP, and retrieval—multimodal pipelines, RAG systems, and evaluation-driven experiments. I care about problem framing, establishing baselines, running controlled comparisons, and understanding why a model behaves the way it does, not just whether it runs.

Through graduate coursework and hands-on projects, I've worked end-to-end across the ML lifecycle: exploratory analysis, feature design, model training, and evaluation. I'm most comfortable in modeling-heavy roles where metrics, error analysis, and trade-offs matter as much as implementation.

Earlier in my career, I worked as a software engineer on a real-time payments platform, contributing to backend microservices, automated testing, and regulated releases. That experience shaped how I approach ML work today— favoring reproducible workflows, clear interfaces, and systems that are safe to operate and evolve.

I'm targeting applied ML and modeling-heavy data science roles, and I'm also open to ML-adjacent backend teams where experimentation and production live side by side.

Specialization Applied ML

Experience Level Mid-level SWE
ML via graduate study and applied projects

Education M.S. CS (ML), Georgia Tech — in progress

Languages English, Spanish

View Projects Let's Talk

Resume

Professional Summary

Applied machine learning practitioner with a strong software engineering foundation and production experience on a real-time payments platform. Currently completing an M.S. in Computer Science (Machine Learning).

Experienced in end-to-end ML/LLM projects, including modeling, offline evaluation, error analysis, and API-based deployment, with a disciplined approach to testing, CI, and release readiness.

Information

Technical Skills

Languages — Python (primary) · Java · SQL · R · C++ (academic)

Backend & APIs — FastAPI · REST APIs · Pydantic · typed request/response models

Quality & Delivery — CI/CD (Jenkins, Maven) · automated testing (JUnit/Cucumber) · API/perf testing (Postman, SOAP UI, JMeter) · release readiness

Cloud & Ops — Docker · AWS (AI Practitioner) · monitoring/alerting · runbooks

LLM / RAG / Agents — RAG pipelines (retrieval, reranking, citations) · evaluation & guardrails · prompt/tool workflows · OpenAI Agents SDK · LangChain (exploratory)

ML Foundations — PyTorch · scikit-learn · Transformers · NumPy · pandas · classification · fine-tuning & transfer learning · sequence models (RNN/LSTM, seq2seq) · attention / memory networks

Data & Evaluation — experiment design · cross-validation · data preprocessing/augmentation · metrics (F1, RMSE, perplexity) · error analysis · visualization (Matplotlib)

Developer Tools — Git · Jupyter · VS Code · IntelliJ · JIRA · Bitbucket · Confluence

Other — English & Spanish (bilingual)

Professional Experience

Software Engineer

JPMorgan Chase & Co. | Tampa, FL

2019 - 2021

Real-Time Payments Platform

Contributed end-to-end across the SDLC for Java-based payments microservices and drove production readiness.
Authored comprehensive operational runbooks—architecture, dependencies/configs, failure modes, diagnostic checklists, and step-by-step remediation—to enable fast triage, recovery, and self-serve on-call; provided light support on monitoring/alerting dashboards with platform/SRE.
Built and maintained test automation (JUnit, Cucumber); enforced CI quality gates (tests, static analysis, coverage) to keep merges green, and executed functional/perf testing (Postman, SOAP UI, JMeter).
Managed regulated releases: compiled release evidence (test/coverage reports, change tickets, approvals) to secure production sign-off; presented services to global production management.
Led/participated in agile rituals (sprint planning, backlog refinement); authored technical docs; coordinated across time zones.

2019 - 2020

Salesforce Platform

Delivered Salesforce CRM data-collection and reporting features for a social-impact organization.
Gathered stakeholder requirements and translated them into schema updates, UI changes, and documented technical implementations.
Expanded the underlying data model by adding new objects, fields, and relationships, and updated the custom Visualforce UI to align with the revised schema for accurate structured data capture.
Contributed a small Flow-based automation to digitize an existing paper survey, enabling standardized survey generation within the CRM.

ML Research Assistant

CS Dept. @ William Paterson University | Wayne, NJ

2018 - 2019

Performed exploratory data analysis on behavioral survey data to complement hypothesis-driven research on teen tanning behaviors.
Cleaned and prepared structured survey data, handling missing values and categorical variables.
Built and evaluated predictive and unsupervised models in Python and R, including Logistic Regression, KNN, and clustering methods.
Compared model behavior and results to understand key drivers, limitations, and trade-offs.
Communicated findings and insights to faculty and student audiences through presentations.

Education

M.S. in Computer Science (Machine Learning)

Georgia Institute of Technology | Atlanta, GA

2022 - Present

Relevant Coursework: Machine Learning, Deep Learning, Natural Language Processing, Reinforcement Learning, Network Science

B.A. in Mathematics | Minor: Computer Science

William Paterson University | Wayne, NJ

2016 - 2019

Relevant Coursework: Applied Regression Analysis, Data Warehouse & Data Mining, Database Management Systems, Cloud Computing

Magna Cum Laude • Pi Mu Epsilon (National Mathematics Honor Society)

Certifications

AWS Certified AI Practitioner

2025 - 2028

Group 2 Social / Behavioral Research Investigators and Key Personnel

CITI Program

2024 - 2027

SWE Resume ML Resume

SWE Preview ML Preview

Projects

All Projects
AI
DL
NLP
UX
Backend / Systems

ProductLens

LLM-driven product comparison tool that ranks products based on user priorities.

GenAI LLM OpenAI Agents SDK Gradio UI

Professional TwinBot AI

AI-powered resume-grounded assistant: tool-style prompting + retrieval patterns for accurate, personalized responses.

GenAI LLM Chatbot Gradio UI

Modular RAG (v2)

Modular, flow-based RAG framework evolved from a monolithic RAG system, designed for hybrid retrieval, explicit orchestration, and agentic extensibility.

FastAPI Hybrid retrieval FAISS LLM