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💡

•Context-Aware QA Chatbot: slashing client data retrieval time by 60% using 
Python, Langchain, and IBM Granite 3.2 LLM, integrating Elasticsearch for 
precise, data-driven answers from Proprietary datasets.
•Enterprise Scalability: End-to-End delivery (development through deployment) of 
scalable Generative AI and LLM solutions, customized to client workflows, 
demonstrably enhancing system performance and operational agility.
•Engineered autonomous AI agents using CrewAI and Llama 3.3, achieving a 40% improvement in decision making accuracy and response timeliness. Architected an intelligent framework that empowered the agent to dynamically select the optimal tool RAG for contextual data, a math tool for calculations, or real-time web search based on the user’s query. This multi-tool capability enabled robust, multi-step reasoning, allowing the agent to overcome base LLM limitations and deliver highly accurate, context-aware solutions.

AI Engineer

Thomson Reuters

•Developed all AI-driven Solutions in AWS cloud(Bedrock).
•GenAI Chatbot Development: Built and deployed AWS cloud-native (SageMaker, 
Bedrock) chatbots using Python, Langchain, and OpenAI (GPT-4 Turbo/GPT-4o), 
enhancing customer NPS score from 8 to 9 via transformer architectures and 
Advanced RAG.
•Enterprise Advanced AI Solutions: Engineered custom prompt workflows and 
Weaviate vector DB integration for optimized query processing, improving 
response accuracy by 25%+ using company-specific data.
•Customer feedback Insight Extraction: Leveraged NLP (Nltk) and LLMs to 
identify actionable pain points from unstructured text, driving data-backed 
product decisions.Leveraging this cutting-edge techniques to unlock hidden 
patterns and trends

Data Scientist

Convertcart

•Driven a 2% increase in overall sales and revenue by developing predictive 
models for customer engagement.
•Acquired and integrated datasets from multiple sources, performing rigorous 
Data Preprocessing to ensure data quality and accuracy.
•Conducted comprehensive Exploratory Data Analysis (EDA), Statistical Analysis, 
and Data Visualization to uncover key insights into customer behavior.
•Applied advanced Feature Selection techniques and Regression Modeling to 
build accurate predictive models for customer engagement scores.
•Implemented Unsupervised Learning (Customer Segmentation) to identify 
distinct customer groups, enabling targeted engagement strategies and 
optimized resource allocation.

Analyttica Datalab Inc.

•Developed and Dockerized deployment of an NLP solution using Python (NLTK, 
scikit-learn, TensorFlow) to automatically prioritize support tickets, significantly 
enhancing operational efficiency through Natural Language Processing.
•Delivered multi-million dollar cost savings and reduced ticket resolution time 
by 100 hours by accurately classifying priority issues using custom-built MLP 
models, demonstrating strong Predictive Modeling capabilities.
•Engineered robust end-to-end ML pipelines involving Text Preprocessing, 
Feature Engineering (word2vec, TF-IDF), and handling Imbalanced Data for 
Machine Learning model development.
•Implemented, evaluated (Model Evaluation), and optimized diverse ML algorithms 
(Logistic Regression, Decision Trees, Random Forest, Gradient Boosting) via 
Hyperparameter Tuning using Python (NumPy, pandas, scikit-learn).
•Managed the full MLOps cycle: instituted monthly model retraining, conducted 
A/B Testing for performance validation, and ensured continuous model 
monitoring in production using Deep Learning (TensorFlow) techniques.
•Proficiently presented technical solutions and impact analysis to stakeholder, 
translating complex Machine Learning technquies and Intelligent automation 
into business value (Stakeholder Communication)

AI Engineer @ IBM | 4 Yrs Exp. in ML, DL, DS, NLP, GenAI & Agentic AI | Ex-Thomson Reuters | VIT Alumni

Senior AI Engineer at IBM with a 4-years of track record of developing and deploying high-impact AI solutions. I thrive at the intersection of Machine Learning, Deep Learning, NLP, and the forefront of Generative AI. My toolkit includes Python, TensorFlow, Keras, and an advanced command of the modern NLP/GenAI ecosystem: LLMs, LangChain, RAG, prompt engineering, sophisticated fine-tuning, and vector databases,Tools,Agents.My analytical prowess is powered by Python (NumPy, Pandas, Matplotlib/Seaborn/Plotly), SQL, and robust data management practices. I am deeply passionate about pushing the boundaries of AI through continuous learning and creating innovative solutions.

taplio

The Battle

💡Timothy Dobbins💡

Achutha Subhash

Followers

Posts

Engagements

Likes

Ready to grow your LinkedIn presence?