Johannes Wilms
Verified Expert in Engineering
Data Scientist and Developer
Vienna, Austria
Toptal member since September 2, 2021
Johannes is a data scientist with a strong theoretical background gained through a PhD in physics and nearly a decade of industry experience in large-scale software engineering projects. He believes in the power of Agile software development to produce results quickly and iterate based on these results.
Portfolio
Experience
- Numerical Modeling - 15 years
- MATLAB - 15 years
- Jira - 9 years
- Pandas - 6 years
- Python - 6 years
- Scikit-learn - 6 years
- Spark - 4 years
- Google Cloud - 2 years
Availability
Preferred Environment
Python, Visual Studio Code (VS Code), Confluence, Git, Jira
The most amazing...
...thing about a project is taking it live and seeing it run in production.
Work Experience
Senior Data Scientist | Data Scientist
Novomatic AG
- Developed a game recommender system to be used in smartphone gaming apps.
- Carried out analyses and developed predictive models for responsible gaming applications.
- Studied and benchmarked models for sports betting predictions, exploring ways to incorporate external information sources to offer more competitive odds or lower operator risk.
- Developed custom online aggregations of Kafka Streams to allow for real-time personalization, incentives, and insights.
- Provided a wide range of in-house data science consulting in the form of analyses and visualizations.
- Integrated data from various SQL and NoSQL sources into a BigQuery data warehouse and created self-service dashboards.
Senior Consultant | Software Consultant
TNG Technology Consulting
- Worked on existing projects with more than one million lines of code.
- Used Jira to support Scrum and Kanban workflows for feature development and bug fixing.
- Supported a CRM system for 10 million+ customers and an eCommerce platform with $500 million+ in annual revenue.
Experience
Recommender System Service
The service consisted of two components. The first component ingested playing data from a PostgreSQL database and generated or updated a model. Typically, this would happen daily because new games were added frequently.
The second component then used the model to serve recommendations in the form of a REST service that provided a number of ways to generate different recommendations for different contexts.
Everything was encapsulated into Docker containers and run on AWS infrastructure. Caching recommendations for performance reasons could be implemented in a straightforward way as a proxy in front of the REST service.
Real-time Personalization and Monitoring
Domain-specific aggregations of the events are used to further personalize the user experience, provide users with custom incentives, and implement real-time monitoring of what's happening on the platform.
Education
PhD in Physics
University of Vienna - Vienna, Austria
Diploma (MSc Degree Equivalent) in Physics
Technical University of Darmstadt - Darmstadt, Germany
Erasmus Study Abroad Year in Physics
KTH Royal Institute of Technology - Stockholm, Sweden
Skills
Libraries/APIs
Scikit-learn, Pandas
Tools
MATLAB, Jira, BigQuery, Tableau, Kafka Streams, Confluence, Git, Subversion (SVN), Miva Merchant
Languages
Python, C++, Java, HTML, JavaScript, Objective-C, Swift, Scala, SQL
Frameworks
Spark, Hadoop
Paradigms
REST
Platforms
Oracle, Docker, Apache Kafka, Kubernetes, Amazon Web Services (AWS), iOS, Android, Visual Studio Code (VS Code)
Storage
Google Cloud, PostgreSQL
Other
University Teaching, Numerical Modeling, eCommerce, Casinos & Gaming, Gambling, Customer Relationship Management (CRM), Predictive Modeling, Dashboards, Recommendation Systems, eCommerce UI, eCommerce UX, eCommerce Platforms, Mobile eCommerce, Telecom Equipment & Solutions
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