Tenet Healthcare

AI Product Director

Trilliant Health

Developed machine learning models to predict various data points in our claims and provider datasets. My role among them was:

• working with Health Plan Price Transparency (HPPT) data to develop a proprietary Rate and Quality Score for payers and providers to enable users to identify who the high-quality *and* affordable providers are
• using machine learning to predict providers' taxonomy classification to enhance our Provider Directory product using claims and NPPES features
• using claims data to infer patients' Primary Service Area (PSA) to enhance many of our products - like our Demand Forecast, Provider Directory, and Network Explorer reaching a ~47% increase in classification accuracy

Data Scientist IV

Unum

• Developed and implemented the company’s first ever AI Governance Framework, aligning AI with enterprise compliance goals and ensuring bias mitigation, model transparency, and reproducibility
• Led the zero-to-one vision, strategy, roadmap, and execution of NLP and AI projects, building scalable AI products across different functional domains while ensuring executive buy-in
• Consistently lead user interviews to discover common pains, needs, and friction points to better inform AI projects

Principal AI Product Manager

• Lead zero-to-one AI projects through Product Discovery, Delivery, and Iteration, while using best Build-Measure-Learn practices
• Orchestrated the cross-functional launch of a scalable NLP-as-a-Service platform (backed by LLMs and GenAI) to address document classification, semantic matching, entity extraction, and summarization leading to a 54% increase over baseline models, a 73% average time savings of common manual processes which drove an annual cost savings of $2.5M
• Championed quick experimentation and iteration on AI initiatives, leading to quicker development and release cycles
• Drove executive buy-in of AI products through heavily-researched operational usability, engineering feasibility, and business viability analyses

Senior Data Scientist / AI Product Manager

Nashville Software School

Data Science Instructor - Assistant (One Cohort)

The General®

•     Led end-to-end 0-1 product development and data science execution across multiple Claims-focused ML initiatives, managing roadmaps, feature definitions, and integration with core systems
•     Identified and prioritized high-impact ML use cases in collaboration with cross-functional stakeholders, translating business needs into scalable predictive models
•     Mentored and coached junior data scientists while also setting standards for model development, engineering best practices, and product delivery
•     Designed, developed, and deployed Silhouette, an internal ML infrastructure tool that automates cloud-based environment setup (AWS), version control (DVC/S3, Git/Bitbucket), and reproducible ML workflows (Pipenv, Cookiecutter)
•     Scaled internal tooling and drove cross-team adoption of Silhouette, reducing friction in deployment and improving reproducibility across ML projects

Lead Data Scientist

•     Shaped data science team vision, product strategy, and foundational infrastructure - establishing the team’s first tech stack, creating its first public-facing AI blog, and launching its first internship program
•     Led zero-to-one development of NLP product using claim notes (Word2Vec, LDA Topic Modeling, Concept Mining) to predict attorney involvement, delivering $2.3M in business savings
•     Built and trained a deep learning model (PyTorch) to classify vehicle damage from claim images, enhancing adjuster workflows
•     Used OCR to extract structured text from scanned claim documents to identify and surface specific types of claim denials
•     Developed a REST API web app to support labeling workflows, enabling scalable image annotation for model training
•     Owned product lifecycle and modeling for a total-loss vehicle prediction system, driving $2.5M in annual cost savings

Data Scientist II

• Perform various natural language processing tasks (R/tidytext, Python/gensim), including sentiment analysis, topic modeling, and Word2Vec/Doc2Vec
• Build a nation-wide price elasticity model using a Mixed Effects/Hierarchical Logistic Regression algorithm (with cross classified and nested random effects) to predict demand for a product at various pricing structures (R, Julia)
• Predict a claim's route by converting claim notes to Word2Vec word embeddings and feeding those word vectors into machine learning models for multi-class prediction (Python/Gensim/Scikit-learn)
• Build an interactive geo-spatial smoothing app (Python/Flask, PostGIS, Leaflet.js) for various key performance indicators using AWS S3 as the data store
• Collaboratively design and implement statistical techniques and machine learning models with team members and department heads
• Create pipeline to transfer data from SQL Server database on local server to PostgreSQL database on AWS EC2 instances
• Assist the data science team in transitioning from local servers to cloud computing on AWS
• Architect and implement RESTful APIs (Python/Flask, Celery, Redis, SQL) that expose various statistical models for consumption

Data Scientist I

Perception Health (Acquired by Huron)

•	Built machine learning models (Python) that used lab data to predict if a patient would be admitted or not and visualized results using an ROC curve and confusion matrix
•	Worked alongside Perception Health's Principal Data Scientist to analyze physicians’ referral patterns and consult hospitals on how to best reduce out-of-network referrals
•	Used GIS (PostGIS, Leaflet.js) techniques to visualize healthcare trends geographically
•	Lead production on Perception Health's flagship product since March 2015 to the present
•	Built customizable, interactive dashboards (Leaflet.js, D3.js, Crossfilter.js) into the primary platform
•	Optimized SQL queries and data preparation for faster graph rendering and page-load times
•	Utilized Agile methodologies (JIRA) to organize releases while ramping up features
•	Worked with the lead architect to optimize SQL scripts across the primary product's platform
•	Converted the platform to a RESTful API for more efficiency and to decouple the server-side code from the client side templates
•	Designed and developed an internal SQL application (Python, Flask) for non-programmers to easily upload data into a database, making repetitive communication with the database faster and more memory efficient

Software Engineer Project Lead

Perception Health

• Built machine learning models (Python, scikit-learn) that used lab data to predict if a patient would be admitted or not and visualized results using an ROC curve and confusion matrix
• Worked alongside Perception Health's Principal Data Scientist to analyze physicians’ referral patterns and consult hospitals on how to best reduce out-of-network referrals

Data Scientist / Software Engineer

Relode, LLC

• Designed and wrote programs that efficiently scraped useful data from various websites using Python and created web applications to be used internally by Relode sales representatives to access this data.
• Uploaded and maintained captured data in a MySQL database.
• Wrote Python scripts that reformated and correctly structured raw, unstructured data.

Software Developer (Contractor)

Digital Reasoning

• Parsed corrupted XML files in Python using ElementTree and Regular Expressions in order to pin point the corruption
• Designed and wrote programs that efficiently cleaned and structured the corrupt data in order to increase the accuracy rate of the firm’s predictive models

Checkd.in

• Employed web-parsing techniques to convert unstructured, raw data into a workable format to perform statistical analyses
• Utilized Facebook's Social Graph API to scrape useful data
• Designed and wrote programs that parsed and statistically analyzed Facebook data using Python

Software Developer (Intern)

Building data products @ Gridsearch | Principal DS @ Trilliant Health

I partner with businesses to bring data and AI-centric products and features to market. 

I also build machine-learning apps with and for my sons. Currently, I’m building a Natural Language Understanding app I call LADNER (Locating Assignments and Dates using Named Entity Recognition) that locates homework assignments and due dates on his teachers’ websites and emails, and then automatically adds them to my Trello calendar daily so we don’t miss homework assignments or quizzes. 

Before that, I built a machine-learning app called Achoo that predicted my son's inhaler usage using air quality, weather, and allergen data.

In my career and hobbies, I build machine learning and data-centric software that makes life easier.

💡Timothy Dobbins💡

Clube FII

🔥 Análise automatizada de carteiras de ativos e investimentos, integrando dados atuais do mercado com históricos, usando IA generativa (LLMs) e bancos de dados relacionais (PostgreSQL, SQL Server).

📊 Desenvolvi pipelines de dados com LLM, permitindo análises comparativas entre ativos e avaliação de impactos de comunicados e dados do mercado nas carteiras dos investidores.

📉 Reduzi o tempo de leitura e interpretação de comunicados em 80% com sistemas de resumos automáticos baseados LLM.

🤖 Criei agentes IA para geração de relatórios financeiros utilizando engenharia de prompt avançada.

🌐 Integrei IA com o front-end (React.js) e APIs RESTful, garantindo comunicação fluida entre módulos.

⚡ Otimizei views SQL e consultas complexas, acelerando relatórios em até 85% e evitando custos com upgrades de infraestrutura.

🚀 Automatizei webcrawlers para extração contínua de dados do Fnet, Banco Central e sites financeiros, com orquestração via Azure Pipelines e armazenamento em AWS S3 e Banco de dados.

🔄 Implementei CI/CD para garantir entregas ágeis e seguras.

🗓️ Colaborei com POs e participei ativamente de dailies, refinamentos e plannings, assegurando entregas alinhadas às metas de produto

📈 Atuação próxima ao PO e demais stakeholders para traduzir necessidades de negócio em soluções robustas

Tech stack: Python (FastAPI, Flask, Pandas, NumPy) • React.js • APIs RESTful & GraphQL • Azure, AWS, Docker • CI/CD (GitHub Actions, Azure DevOps) • Hugging Face • LangChain, LangGraph, RAG, vector DBs.

Especialista em IA

- Gereis insights de clientes e oportunidades de negócio aplicando ciência de dados, estatística e inteligência de mercado.

- Apliquei machine learning, deep learning e data mining para análises preditivas e segmentações.

- Implementei pipelines de deploy com Azure Functions, entregando soluções escaláveis.

- Executei web scraping com Scrapy, modelagem de dados e consultas SQL otimizadas.

- Atuei com arquitetura limpa, SOLID e testes unitários, garantindo qualidade contínua.

Data Scientist

- Desenvolvi páginas de dados financeiros e painéis interativos com VB.NET, JavaScript, HTML e CSS.

- Realizei ingestão de dados via APIs JSON e manipulação eficiente com SQL Server, processando ~400 itens em apenas 3 segundos.

- Modelei lógica de extração, transformação e carga (ETL) com alto desempenho.

- Atuei no onboarding técnico de desenvolvedores iniciantes, com mentoria em lógica de programação, Git, boas práticas, resolução de bugs e soft skills.

Software Engineer

self-contained

Civil Engineer

Construtora Nobe

Trainee

Seu Software

Engenheiro de software

Data Science

Especialista em Inteligência Artificial e Automação, com expertise em construir agentes inteligentes, pipelines de dados e integrações corporativas usando LLMs como ChatGPT, Gemini, LLaMA e frameworks como LangChain, LangGraph e Hugging Face.

Minha missão é transformar fluxos de trabalho complexos em automações inteligentes com RAG pipelines, embeddings e vector databases (Pinecone, Weaviate, FAISS).

💻 Tech stack:
Python (Flask, FastAPI, Pandas, NumPy) • SQL (PostgreSQL, SQL Server) • APIs RESTful & GraphQL • Front-end (React.js, JavaScript, TypeScript, HTML5, CSS3) • Azure, AWS, Docker, GitHub Actions, Azure DevOps (CI/CD) • LLMs (ChatGPT, Gemini, LLaMA) • Hugging Face, LangChain, LangGraph, RAG pipelines, embeddings, vector databases (Pinecone, Weaviate, FAISS)

🚀 Projetos recentes com LLM:
🔹 Análise automatizada de carteiras de ativos e investimentos combinando dados atuais do mercado, séries históricas e ativos existentes para gerar insights estratégicos.
🔹 Modelagem de impacto de comunicados oficiais (CVM, B3, Fnet, Banco Central) sobre carteiras, identificando padrões e tendências.
🔹 Construção de comparativos dinâmicos entre ativos, integrando dados históricos e tempo real em bancos de dados SQL Server e PostgreSQL.
🔹 Desenvolvimento de pipelines de dados para análises contextuais ricas e geração de relatórios em linguagem natural.
🔹 Criação de agentes IA para geração de conteúdo, assistentes virtuais e análises preditivas, entregando soluções escaláveis e auditáveis com supervisão humana.


🎯 Objetivo: desenvolver soluções inteligentes que transformam dados complexos em decisões estratégicas, com ética, performance e visão de longo prazo.

taplio

The Battle

💡Timothy Dobbins💡

Tulio Vargas

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