Data Science / ML Consultant at Alexander Thamm GmbH

Additional Description of Tasks

Built the company's first product from scratch — an MVP sales forecasting application — combining XGBoost, Prophet (Bayesian forecasting), and deep learning (CNNs, RNNs) in Python, using Flask and Plotly Dash for the deployment and user interface. Presented findings directly to the client.

Built a data collection, pre-processing, and analysis pipeline from scratch to predict customer conversion likelihood using logistic regression, random forests, and XGBoost, with an emphasis on accurate yet explainable models in the retail market.

Developed and demoed an NLP-based product to analyze and correct improper spellings in the German language.

Assisted in the creation of a pipeline using Kubeflow and CNNs to classify chest X-rays as healthy, COVID-19, pneumonia, or other diseases.

Researched and documented the benefits and trade-offs of 8 ML platforms (AWS SageMaker, Azure Databricks, Kubeflow with MLflow, etc.), building a scoring system across exploration, pipelines, model management, deployment, batch inference, and monitoring.

• Location: Cologne, Germany
• Start Date: January 2020
• End Date: December 2020
• List of Relevant Technologies Used: Python, R, Linux OS, Git, AWS SageMaker, AWS CodeCommit, AWS S3, Docker, Kubernetes, Plotly Dash, Flask, Tensorflow, Keras, huggingface (NLP), XGBoost, Pandas, Numpy, ggplot2, dplyr