Senior Data Scientist

I am a team-oriented data and machine learning expert with over a decade of experience in leading and contributing to the full life cycle of cutting-edge data solutions. I have a proven track record of collaboration and adaptability

• Business Intelligence (BI)9 J.
• Cloud Computing
• Computer Vision
• Data Science
• DevOps (allg.)
• Maschinelles Lernen
• Microsoft Azure
• Natural Language Processing
• Predictive Analytics
• Software engineering / -technik

I am a team-oriented data and machine learning expert with over a decade of experience in leading and contributing to the full life cycle of cutting-edge data solutions. I have a proven track record of collaboration and adaptability, having worked with some of the biggest German organizations, including Allianz, Telefonica O2, Adidas, Boehringer Ingelheim, Innogy (RWE), and Eon.
I love working with data and thrive on contributing to the growth of an organization in a dynamic and collaborative team believing that the whole is greater than the sum of its parts. My ideal is to excel in following areas
• Deep Learning and Optimization
• Full Stack and Cloud Technologies
• Communication and Presentation Skills
• Problem-solving and Analytical Thinking
• Cross-functional Collaboration
• Time Management and Prioritization
• Leadership and Mentoring
Throughout my career, I have demonstrated the ability to work with diverse data sets, including structured data, images, text, video, time series, and geospatial data sets. My experience spans various industries such as insurance, health, energy, telecom, and ecommerce. In addition, I am well-versed in major technology stacks.
In my previous roles, I have successfully delivered complex projects that required close collaboration with cross-functional teams. As a senior data scientist, I have also provided guidance and mentorship to junior team members and contributed to the overall success of the organization.

Machine Learning Algorithms: Linear Regression, Logistic Regression, SVM, Decision Trees, k-NN,
Clustering (K-means, DBSCAN), PCA, t-SNE, Anomaly Detection,

Supervised Learning: Random Forest, Gradient Boosting, XGBoost, LightGBM.
Time Series Analysis: ARIMA, SARIMA.
Recommender Systems: Collaborative Filtering, Content-based Filtering, Matrix Factorization.
Model Evaluation Techniques: Cross-validation, Hyperparameter Tuning, Regularization.
Deep Learning Frameworks: PyTorch, Keras.
Neural Networks: MLP, CNN, RNN, LSTM, GRU.
Natural Language Processing (NLP): Word2Vec, GloVe, BERT, GPT, T5, spaCy, NLTK, Hugging Face Transformers.
Computer Vision: Image Classification, Object Detection, Segmentation, OpenCV.
Reinforcement Learning: Q-Learning, DDPG, PPO, A3C, Stable Baselines, RLlib.
Generative Models: GAN, VAE, StyleGAN.
Tools and Infrastructure: Apache Spark, Dask, Hadoop, Docker, Kubernetes, Mlflow, CI/CD.
Visualization: Tableau, Dash, Shiny.
Cloud Platforms: AWS, Google Cloud, Microsoft Azure.
Project Management: Jira, Kanban.
Development Languages: Python, R, Scala, JavaScript.
Version Control: Git.
Scripting: Bash.