I'm a computer scientist interested in everything related to data science, including the development of algorithms and the application of state-of-the-art techniques to scientific and business problems in web applications. Whereas I'm more of a science guy, I'm also extremely interested in big data and the implementation of complex, scalable systems. That is why distributed systems of any kind, including well-known database systems, are of great interest to me. I find interdisciplinary teams fun to work with because I enjoy acquiring experience outside of my core abilities. Machine Learning and Data Mining: Scikit-learn, Weka, Pandas, Dask, LibROSA, Numba, Scipy, NumPy, TensorFlow, PySpark, Deduplication with Splink, GraphFrames, and Matplotlib. Data Engineering: Google Cloud Platform (Cloud Storage, Cloud Functions, BigQuery, Dataproc), Cloudera (Hadoop, Spark, Impala, Hive, Sqoop), Apache Airflow, Dagster, Scrapy, Selenium, Azure (Data Factory, AKS), Prefect, Snowflake, Amazon Web Services (AWS). Web Development: HTML5, CSS3, JavaScript, JQuery, AngularJS 2, React, Django, Web2py, PostgreSQL, MongoDB, SQLAlchemy, Bootstrap, Ruby on Rails, CodeIgniter, and Flask. Mobile Development: Apache Cordova. Software Development Methodologies: RUP, Scrum, and DevOps for software engineering in general, Kimball for data warehouses, and MLOps for ML. InnerSource, Data Governance, and Data Lineage. Programming Languages I've used the most: Python, Scala, Java, Haskell, JavaScript, and C. Architectural patterns: Microservices. Other tools: Trello, Power BI, Asana, Jira, Confluence, Git, Docker, Jenkins, Buildkite, Cloud Build, Terraform, Kubernetes, Helm, Redmine, and UML.