Philipp Schmidt
Head Of Telco, Media & Entertainment, Games, And Sports AWS Germany at Amazon Web Services (AWS)
Hamburg, Germany
Summary
🤩
Rockstar🎓
Top SchoolPhilipp Schmidt is a seasoned technology and commercial leader with 11 years in enterprise and cloud sales, now heading Telco, Media & Entertainment, Games, and Sports for AWS Germany. He blends deep industry go-to-market experience at AWS with earlier client-facing roles at Dimension Data and avodaq, consistently scaling relationships across complex enterprise accounts. Technically fluent from a Medieninformatik background, he contributes to open-source projects like awslabs/deequ and Keras, showing hands-on skills in data quality and ML training tooling. That dual commercial-technical profile helps him translate product capabilities into measurable business outcomes for media and gaming customers. Based in Hamburg, he is known for bridging engineering teams and C-level stakeholders to accelerate cloud adoption. He brings a pragmatic, test-driven mindset to strategic sales, evidenced by contributions to testing and anomaly-detection code in open-source repos.
11 years of coding experience
15 years of employment as a software developer
Dipl. Medieninform. (FH) Medieninformatik, Dipl. Medieninform. (FH) Medieninformatik at Fachhochschule Wedel
Github Skills (20)
unit-testing10
spark10
data-quality10
python10
data-science10
machine-learning10
scala10
callback10
keras10
deep-learning10
trainings10
tensorflow10
neural-network10
unit-test10
modeling10
Programming languages (4)
Github contributions (5)
awslabs/deequ
Sep 2018 - Nov 2018
Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
Role in this project:
Back-end Developer / Data Engineer
Back-end Developer / Data EngineerContributions:4 releases, 16 commits, 20 PRs in 2 months
Contributions summary:Philipp primarily contributed to the `deequ` library, which is built on Apache Spark for data quality analysis. Their commits involved fixing typos, adapting non-negative and positive checks, and implementing additive Holt-Winters for anomaly detection. The user also made changes to the test suite, particularly within the `CheckTest` class, suggesting active involvement in testing and validating data quality constraints.
data-qualityapacheunitsparkunit-tests
keras-team/keras
May 2015 - Jun 2015
Deep Learning for humans
Role in this project:ML Engineer
ML EngineerContributions:30 commits, 12 PRs, 35 comments in 20 days
Contributions summary:Philipp's contributions primarily focused on enhancing the Keras library's functionality related to model training and loss functions. They added features such as class weight support to the `Model` class and objective functions, which allows for more nuanced training. Further, they introduced the `ModelCheckpoint` callback and sample weights, demonstrating a focus on improving model training flexibility and robustness. This user also implemented shape checks for `set_weights` and other refactoring tasks.
pythondata-sciencedeep-learningneural-networksmachine-learning
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