I am a data scientist at Provinzial where I work on RAG systems and domain-specific AI applications. I am driven by curiosity and approach my work with a scientific mindset. I enjoy deep work and continuously learning new things.

I hold a PhD in Accounting. In my research, I have broadly focused on measurement problems in accounting. I have applied traditional quantitative methods as well as novel techniques from machine learning, natural language processing, interpretable machine learning, and causal machine learning to evaluate the prediction of accounting estimates, the analysis of corporate narratives, and the estimation of causal effects.

1. Data Ingestion & Processing
   Web Scraping: rvest, Beautiful Soup, Selenium
   Streaming: Apache Kafka
   Query Languages: SQL

2. Data Analysis & Visualization
   Data Wrangling: pandas, tidyverse
   Plotting Libraries: ggplot2
   Dashboards & Visual Analytics: Kibana

3. Machine Learning & Statistical Modeling
   Machine Learning: tidymodels, scikit-learn, DoubleML
   Model Interpretability: DALEX
   Data Labeling: prodigy

4. Deep Learning & Natural Language Processing
   DL Frameworks: pytorch, transformers
   NLP: spacy

5. Experimentation & Prototyping
   Experiment Tracking: Weights & Biases
   Interactive Prototyping: gradio

6. Reproducibility & Documentation
   Literate Programming: Jupyter Notebooks, R Markdown

7. Software Engineering & APIs
   API Development: FastAPI
   Version Control: Git

8. MLOps & Deployment
   CI/CD & Orchestration: Kubernetes, ArgoCD

9. Infrastructure & Observability
   Monitoring & Logging: Dynatrace, Kibana
   Search & Analytics Databases: Elasticsearch