01.01.2025 aktualisiert
100 % verfügbar
Data Science / Data Engineering / Machine Learning Engineering
Düsseldorf, Deutschland
Deutschland
PhD in Electrical Engineering and Information Technology (Dr.-Ing.)Skills
Machine LearningPython Data ScienceAzure MLAzureDatabricksSparkData Analytics PandasArtificial Intelligence
Technologies: Python, Pandas, Numpy, Scikit-learn, XGBoost, Spark, Databricks, Prophet, MLflow, Azure ML, Matplotlib, Seaborn, Plotly dash, SQL, Docker, Kubernetes, Brigade, Azure, Azure DevOps Pipelines, Git, Linux, MacOS, SCRUM
For more information, please have a look at the full CV including a project list.
For more information, please have a look at the full CV including a project list.
Sprachen
DeutschMutterspracheEnglischverhandlungssicher
Projekthistorie
In-house Data Science consulting covering a broad range of use-cases across the organization:
- Lead and contributed to several Data Science / machine learning POCs and projects with different business contexts: customer credit risk assessment, smart debt collection, product quality optimization, chemical experiment design and formulation optimization, energy demand forecasting, hair health assessment from sensor measurements, etc.
- Mentored junior data scientists in multiple projects on problems concerning machine learning techniques, infrastructure, deployment, and scaling.
- Consulted data science use-cases in the ideation phase and conducted quality assurance on projects with external implementation partners in all phases of the project lifecycle
- Lead an initiative to standardize the team internal data science toolchain. This included requirement analysis, evaluation of platform options (AzureML and Databricks), deploying and configuring an experimental AzureML environment, creating a demo application and conducting training sessions for the team. Contributed to automation of AzureML workspace deployment.
- Advocated the application of principles from software engineering and DevOps in machine learning projects. Pioneered Spark (Databricks) CI / CD workflows including unit and integration testing in close collaboration with Henkel's DevOps team.
- Contributed to the digital upskilling within the Finance department with two separate presentations about machine learning use-cases in Finance that were broadcasted to hundreds of Henkel's employees.
- Created a data challenge that was used as part of the recruitment process for the team and regularly assisted and conducted technical interviews.
- Technologies: Python, Pandas, Numpy, Scikit-learn, XGBoost, Spark, Databricks, Prophet, MLflow, Azure ML, Matplotlib, Seaborn, Plotly dash, BoTorch, Scikit-Optimize, MS SQL, Docker, Kubernetes, Brigade, Azure, Azure DevOps Pipelines, Git, Linux, MacOS
Data Science consulting and machine learning backend development in a data science startup:
- Exploratory data analysis of customer data such as financial time-series and sensor data from chemical production plants.
- Built time-series forecasting models to predict sales for a customer in the agricultural sector.
- Developed a new anomaly detection sub-module for the in-house data science platform.
- Agile development (SCRUM) for the machine learning backend of the in-house data science.
- Setup and maintenance of continuous integration (CI) for the in-house data science platform with Jenkins and Docker.
- Contributed to the Dask open-source project by reporting and fixing bugs.
- Technologies: Python, Pandas, Numpy, Scikit-learn, XGBoost, Prophet, Matplotlib, Seaborn, Dask, Celery, Docker, Jenkins, MongoDB, SQL (Exasol), Redis, RabbitMQ, Lua, Kafka, Git, Linux, SCRUM