Mike Hirsch
Senior Machine Learning Engineer at Philadelphia Phillies
Philadelphia, Pennsylvania, United States
Summary
🤩
Rockstar🎓
Top SchoolMike Hirsch is a Senior Machine Learning Engineer with 11 years of experience building production ML and data engineering solutions across sports, finance, and enterprise telemetry. Currently at the Philadelphia Phillies, he applies advanced analytics to real-world problems after leading ML and UX capabilities at Elastic and serving as a principal quantitative strategist at Vested. His background spans research in statistical learning and decision trees to hands-on ETL and data ingestion work—evident from contributions to elastic/eland where he implemented CSV-to-Elasticsearch pipelines and EDA notebooks. Known for bridging research and product, he has led data science teams for major brands like Nike and Uber and brings a pragmatic focus on shipping reliable, interpretable models. He holds a BA in Mathematics from NYU and combines academic rigor with production-first engineering instincts.
11 years of coding experience
9 years of employment as a software developer
Exchange, Mathematics, Exchange, Mathematics at Trinity College, DublinBachelor of Arts (BA), Mathematics, Bachelor of Arts (BA), Mathematics at New York University
Github Skills (14)
data-analysis10
elasticsearch-api10
amazon-elasticsearch10
etl10
elasticsearch10
elasticsearchquery10
aws-elasticsearch10
python10
data-engineering10
dataframe9
jupyter-notebook9
pandas9
dataframes9
big-data9
Programming languages (7)
Github contributions (5)
elastic/eland
Nov 2019 - Nov 2019
Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch
Role in this project:
Data Engineer
Data EngineerContributions:11 commits, 1 issue in 6 days
Contributions summary:Mike primarily contributed to the project by adding functionality to load and analyze data within an Elasticsearch context. They implemented a Python script to load data from a CSV file into an Elasticsearch index, including data parsing, schema definition, and bulk indexing. Further work included the creation of a Jupyter Notebook for exploratory data analysis (EDA) of the loaded data, demonstrating their focus on data ingestion and analysis using tools like eland.
data-analysisdataframespythondata-sciencedataframe
elastic/anonymize-it
May 2018 - Jan 2021
Contributions:6 reviews, 41 commits, 7 PRs in 2 years 8 months
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