Jens Daube - Product Owner & Senior Data Scientist
Experience
Product Owner & Senior Data Scientist
Legal Tech
- Led an international team of six developers in a Scrum environment
- Defined strategic goals for the project in coordination with stakeholders and the development team
- Prompt engineering for language models to improve the accuracy and relevance of generated responses
- Implemented LangChain components for a RAG chatbot to answer legal questions
- Technologies: GPT-4, LangChain, Python (Pandas, sklearn, streamlit), Docker, GitLab, ChromaDB
Senior Data Scientist
Central Bank
- Developed a scalable and high-performance enterprise search solution
- Implemented a retrieval-augmented generation (RAG) model to deliver valid, context-aware answers to user queries about documents
- Implemented and managed messaging queues to ensure reliable and scalable data processing and transfer between different system components
- Created RESTful APIs to provide search functionality and integrate the enterprise search solution into existing applications and systems, including security and authentication mechanisms
- Technologies: ElasticSearch, Kibana, LLaMA, SQL, FastAPI, Docker, Python (Pandas, sklearn, PyTorch), HuggingFace
Senior Data Scientist
Financial Supervisory Authority
- Developed and implemented an early warning system based on structured and unstructured data to monitor fund default risk
- Developed a GPT-4-based chatbot for the authority to answer questions about annual and quarterly reports
- Implemented and configured automated CI/CD pipelines to automate build, test, and deploy processes
- Worked closely with subject matter experts to understand the requirements for the early warning system
- Technologies: GPT-4, LangChain, Python (Pandas, NumPy, PyTorch, sklearn), SQL, GitLab, Docker, Kubernetes, Apache Spark, ChromaDB
Senior Data Scientist
Public Authority
- Led the project, regularly coordinated with the client, and ensured all requirements and expectations were met
- Developed and trained models to analyze economic and financial market reports
- Optimized model performance through hyperparameter tuning and implementation of feature engineering and regularization
- Collaborated with experts to validate model results and adapt them to the authority's specific requirements
- Technologies: Python (Pandas, Numpy, SpaCy, sklearn, Keras), HuggingFace, GitLab, Docker
Senior Data Scientist
Beverage Manufacturer
- Conducted in-depth analysis of historical sales data to identify patterns, trends, and seasonal fluctuations that could affect sales figures
- Used time series analysis techniques like ARIMA, exponential smoothing, and advanced ML models like random forests and LSTMs to improve sales forecast accuracy
- Incorporated additional data such as weather data and marketing campaigns to further improve forecast accuracy
- Performed sensitivity analyses and scenario modeling to identify potential risks and opportunities early and develop appropriate strategies
- Technologies: Python (Pandas, NumPy, seaborn, sklearn), GCP (Dataproc, BigQuery, Cloud Functions, Vertex AI), SQL, GitLab
Senior Machine Learning Engineer
Universal Bank
- Development and implementation of preprocessing pipelines to standardize and structure traders' communication data
- Use of the pretrained FinBERT model to generate word embeddings from finance-related texts
- Development and implementation of models for network analysis, anomaly detection, and clustering
- Development and automation of end-to-end workflows for model training, validation, and deployment
- Technologies: Python (SpaCy, sklearn, TensorFlow), Hugging Face, SQL, ElasticSearch, Docker, Kubernetes, GitHub, Jenkins, MLflow
Data Scientist
Traffic Data Authority
- Design and implementation of cloud-based system architectures with Azure
- Setup and configuration of Kubeflow and MLflow to manage and automate machine learning workflows
- Creation and training of machine learning models to identify unusual traffic patterns and situations
- Development and execution of tests to ensure functionality, reliability, and security of the developed solutions
- Technologies: Python (TensorFlow, PyTorch, Pandas, NumPy), Azure (Kubernetes Service, DevOps, Storage), Kubeflow, MLflow, Helm
Machine Learning Engineer
Automotive Group
- Collection and cleaning of historical sales data as well as external factors like market trends, economic data, and seasonal influences
- Identification and creation of relevant features to improve the prediction accuracy of the models
- Development and training of various machine learning models to forecast sales figures, including specific forecasting models like Prophet and ARIMA
- Implementation of an explainable AI module based on SHAP to improve transparency and interpretability of model results
- Technologies: Python (Prophet, statsmodels, Keras, Pandas, NumPy, SHAP), SQL, GitLab
Data Scientist
Asset Manager
- Development of Dockerized microservices, including APIs and test specification for named entity recognition, using and adapting pre- and post-trained AI models
- Development and implementation of models to calculate a sentiment score for fund reports
- Identification and creation of relevant features from text data that contribute to improved model performance, as well as selection of the most important features for model training
- Optimization of hyperparameters through systematic search or advanced methods like Bayesian optimization
- Technologies: Python (Pandas, NumPy, NLTK, SpaCy, TensorFlow), Flask, Azure (Databricks, Cognitive Services, Machine Learning, DevOps)
Data Scientist
Universal Bank
- Creation of a workflow for processing document data, including seamless integration of OCR and NLP modules
- Implementation of OCR algorithms for automatic text recognition in various image file formats (tif, jpg, png), including containerization of OCR microservices using Docker
- Development and implementation of NLP models for information extraction from recognized texts
- Extraction of relevant features from OCR and NLP data that can contribute to improved model performance and better information extraction
- Technologies: Python (Tesseract, SpaCy, NLTK, Pandas, NumPy), Docker, Kubernetes, GitLab
Data Analyst
Kreditbank
- Developed and validated credit risk models in Python, including implementing Monte Carlo simulations to analyze different risk scenarios
- Used SQL to manage and query large datasets, followed by data preparation, cleaning, and exploratory data analysis in Python to identify key features and patterns
- Validated models through backtesting and historical data analysis, then fine-tuned models based on validation results
- Integrated the developed models into the bank's IT system and deployed them for production use, including continuous monitoring and optimization
- Technologies: Microsoft SQL, Python (Pandas, NumPy, SciPy, sklearn, Seaborn), GitLab, Docker
Industry Experience
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Experienced in Banking and Finance, Information Technology, Professional Services, Government and Administration, Food and Beverage, and Automotive.
Business Area Experience
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Experienced in Information Technology, Business Intelligence, Project Management, Product Development, Research and Development, and Quality Assurance.
Languages
Education
University of Mannheim
Master in Data Science · Data Science · Mannheim, Germany · 1.3
University of Mannheim
Bachelor of Science in Business Mathematics · Business Mathematics · Mannheim, Germany · 1.7
Certifications & licenses
Professional SCRUM Master 1
Statistics
Experience
Global Experience
Expertise
Qualifications
Profile
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