Hello, I'm Michael. I'm a full-stack data scientist/engineer with 10+ years of experience. I enjoy building data sites & apps.
About Me
I am passionate and results-driven with a knack for transforming data into actionable insights. With a formal background in Mathematics & Statistics and modern data and development practices, I thrive on solving complex problems and making data work for businesses at scale.
My journey has taken me through diverse roles, from being the Director of Data Science for a Private Equity firm specializing in music content rights to leading data science teams at global companies like Spotify. I've helped companies leverage data to secure investor funding, optimize strategies, and create innovative solutions.
Whether it's developing predictive models, conducting AB tests, designing ETL pipelines, or building dashboards, I am dedicated to turning data into a strategic asset. In addition to my day-to-day work, I also run a sports analytics data platform, Ai Athlete. Feel free to reach out for more info!
When I'm not working, I enjoy a variety of hobbies. I'm passionate about sports, music, dogs, and DIY projects. I play the euphonium and drums and love traveling, learning new things, and bingeing Netflix. Oh, and one fun fact: I'm a Guitar Hero Legend.
My projects
Ai Athlete
I worked as a full-stack developer on this startup project for 2 years. Provides daily predictions, player trends, hall of fame comparisons, and trivia games.
- Python
- Streamlit
- Vega-Lite
- Tensorflow
- MindsDB
My skills
- Python
- R
- SQL
- DBT
- GA4
- Dashboards (Metabase)
- Streamlit
- Git
- DuckDB
- Tensorflow
- Terraform
- Prefect
- Airbyte
- Self-Hosting
- NextJS
My experience
Graduated College
Rutgers University: New Brunswick, NJ
I graduated with a degree in Math/Statistics
2014Statistics Analyst
ESPN: Bristol, CT
Collaborated with the stats and analytics team to deliver accurate and timely sports statistics for live broadcasts and digital platforms. Developed automated workflows and tools to streamline data collection and reporting, ensuring data integrity under tight deadlines.
2014 - 2015Data Scientist
JPMorgan Chase: New York, NY
Applied advanced statistical models and machine learning algorithms to optimize financial processes and drive data-informed decisions. Designed and implemented ETL pipelines, predictive analytics models, and dashboards to improve business efficiency and risk management.
2015 - 2017Senior Data Scientist
Spotify: New York, NY
Led data science initiatives to enhance user experience and optimize content strategies for Spotify's global audience. Developed and implemented machine learning models to personalize playlists, predict user behavior, and maximize engagement. Collaborated with cross-functional teams to design AB tests and deliver actionable insights that drove product innovation and business growth. Played a key role in projects like 'Family Mix,' creating algorithmic playlists shared among family plan users.
2017 - presentContact me
Please contact me directly at michael@nestel.me or through this form.