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💡

Fabian Werkmeister

FUNKE

• Tech-lead for digital campaign performance evaluation project
- Led development and integration of multiple campaign performance data sources into a central data platform
- Developed data model for campaign performance evaluation
- Enabled departments to gain unified view of digital advertising campaign performance, which wasn’t possible before
- Implemented self-service analytics & insights for departments

• Developed a data model for subscription retention and implemented a visualization tool for departmental access

• Contributed to a development team that achieved a fully automated integration of various data sources into the Google Cloud environment

• Managed the continuation and expansion of a logistics project using algorithmic route optimization

• Consulted business departments to translate their problems into data problems and build sustainable solutions for that

Data Engineer

Trainee program in Data Engineering & Science

• Integrated various data sources into a central data platform, building data pipelines and ETL workflows

• Ingested news from event and sports reporting into machine news processing workflows in the GCP, enriching the data with NLP techniques to improve reporting quality

• Led a proof of concept for the volume prediction of advertising bookings in daily newspaper planning

• Consulted business departments on opportunities for data projects

Data Trainee

neuefische GmbH - School and Pool for Digital Talent

Fulltime intensive coding bootcamp:

• 540 hours of coding practice
• Individual end-to-end machine-learning project as "journeyman's piece"
• Tech-stack ➟ Python, pandas, NumPy, scikit-learn, TensorFlow, Keras, matplotlib, seaborn, bokeh SQL, AWS

Trainee Data Science

Toluna Corporate

Technical Expert for all digital market research solutions:

• Responsiblity for national & international customer relations and projects
• Trained customers & colleagues on digital market research platform
• Technical expert for data preparation and -processing for custom reports and dashboards

Engagement Manager

infiniteFox

Trainee program in search-engine-marketing:

• Responsibility for Google Ads ad-spend on customer accounts
• Setup, reporting and analysis of Google Ads campaigns
• Fundamentals of Web Analytics & Tracking

Trainee Performance Marketing

Q1 Energie AG

Practical master thesis in strategic customer loyalty:

• Independently conducted market research study with 500+ interviews
• Identified market segments by performing cluster analysis in R
• Proposed a strategy for implementing a customer loyalty program based on study insights

Masterand Business Development

I help you to become fluent in Data and enable you to translate between business & tech ★ Data Scientist @FUNKE ★ 🐍Python 🤖Data Science 🛠️Data Engineering

TLDR; In just 3 years I went from total data newbie to go-to-expert for all data topics at my company by using learning systems --- Want to achieve the same?! 👉 Follow me

—————————————————————————

Hi, I’m Fabian 👋

3 years ago I came fresh out of a 12-week data science bootcamp and was totally hooked on everything related to data

I wanted to learn every single concept, do every course and finally become the data semigod!



Looking back, this was a very romantic view (some would call it naive)

With every new task and project in my first job, my learning process slowly stagnated

And all of a sudden, without me noticing, it came to a complete hault

- I had little free time to learn new things
- With so many new tools and technologies emerging all the time, I simply didn’t know where to start and where to stop
- Even if I found the time to learn new stuff, I had no idea how to apply it to my job tasks to make it really stick
--> often enough I simply forgot the new stuff after some weeks
- Since most topics went beyond basic stuff and became much more complex than what I did in bootcamp it became quite intimidating to actually start to learn these things
- And finally with demanding workloads and tight deadlines, I completely lost sight of the long-term benefits of learning

Once I realized this, I fell into the rabbit hole of productivity, time-management, self-organization, habits and especially the concept of lifelong-learning

3 years forward - I never came out

I developed learning systems, which enabled me to learn the most important things in each situation as fast, focused and sustainable as possible for me - all while staying in balance with my energy and my actual work task.



All of this enabled me to learn and do nearly everything in the data value chain on my own without having a clear guidebook, which told me what to do or what is important

My name at FUNKE became associated with data and I became the go-to-expert for all data-related questions and projects

I am no expert on any particular data topic and I may not know what will be important in the upcoming years, but with my learning systems and attitude towards lifelong-learning I am more than well prepared to learn any topic & skill I stick my head into.



Follow me for actionable content on time-management, productivity, applied learning, AI-powered learning systems and the right mindset to achieve the same!

Data Scientist

For years, I was convinced I was broken.
Then I learned that I'm a Highly Sensitive Person.
 

Nearly my whole life everything felt just … too much.
I often heard sentences like “don’t be so sensitive” or “just get over it”.
Why did I get so overwhelmed by things no one else seemed to notice?

I felt something was wrong with me, until I read a book about highly sensitive persons.
For the first time in my life I felt really understood.
And I realized that I’m not alone with this.

Sensitivity is not a choice or a feeling.
It’s a neurological trait.
It means my nervous system processes the world more deeply and thoroughly than average.
➡️ I don't just hear noise, my brain absorbs it.
➡️ I don't just see a problem, I process every single potential angle
➡️ I'm an "empathic sponge", I literally feel tension between humans

In a data career, this is a daily battle.
A battle between pragmatism and perfectionism.
A battle between communication and overstimulation


But I learned that it can also be a hidden superpower:
✅ I don't just fix a bug, I find the systemic flaw in the source data that caused it.
✅ I can feel and process the user's frustration, so I'm driven to build solutions that actually solve their pain, not just the technical ticket.
✅ Since chaos is physically draining to me, I naturally build a clean, organized, and well-documented infrastructure

I stopped fighting my wiring.
Now I use it as a tool to build awesome stuff!


Any other HSPs in data out there? 
What's your superpower? :)

----------------------------------------------------

Hi, I'm Fabian👋

I write about building lean and cost-effective analytics platforms on GCP

If you like my content
👉 hit Follow
♻️ share the post with others
🤝 write me a message and connect with me

Lean & cost-efficient GCP analytics platforms for businesses to finally get results from their data | 🛠️ Data Engineer @ FUNKE

I was thinking all wrong about spreadsheets.
Excel and Sheets are my most valuable connectors to the business.

Tech teams often see spreadsheets as a dirty data source.
Business teams see spreadsheets as the only data source.
In reality it is a powerful bridge.

Here are 2 ways to use it as a serious data tool:

1️⃣ Mapping (my favourite use case):
Mappings (e.g. product id to product category) are often dynamic and can change quite a lot.
So instead of keeping this logic buried in your code, keep it in a spreadsheet and ingest it directly into your pipelines.
That way you and your stakeholders can keep the mappings up to date and all changes are directly taken to your workflows.

2️⃣ Query the DWH / BI-Tool
Enable your business stakeholders to get the data they need and let them do their analysis wherever they feel the most comfortable.
This can be done as a direct connection to the data warehouse or by setting filters in a dashboard and then exporting the resulting data


Stop with the data snobbery about spreadsheets.
And definitely stop seeing Sheets as your sworn enemy.
Start seeing it as your most valuable connector between business and tech.

---------------------------------------------------------------

Hi, I'm Fabian 👋

I write about building lean and cost-effective analytics platforms on GCP.

If you like my content
👉 Follow Fabian Werkmeister
♻️ share the post with others
🤝 write me a message and connect with me

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