Data Scientist2019 - PRESENTSelf-employed
Technologies: Jupyter Notebook, PyCharm, HTML, Beautiful Soup, Sublime Text, Process Automation, Dashboard Design, Data Visualization, Dashboard Development, Dashboards, Data Analysis, Selenium, Automation, CSS, Kotlin, Android Development, Web Crawlers, Web Scraping, Data Science, Pandas, Machine Learning, Data Analytics, Git, Python, RStudio, Data Engineering
- Consulted about the application of machine learning in concrete use cases (pharmaceutical industry).
- Implemented and diagnosed machine learning models with frequent presentations of results to the client.
- Automated the analysis pipeline in a medical study.
- Scraped a specific website containing lots of data in HTML format and built a database based on this data.
- Developed an Android application to organize courses and track and visualize evaluations for a specific business use case.
- Built an automated system to evaluate the long-term performance of a machine learning model based on weekly new data.
Data Scientist2020 - 2021Focus Sensors Limited
Technologies: Python, Algorithms, Apache Kafka, SciPy, Testing, Streaming Data, Signal Processing, Docker
- Examined the implementation and the mathematical concepts of an extensive codebase of an anomaly detection algorithm of sensor data.
- Changed the core architecture from static data files to streaming data using Kafka.
- Tested and optimized the new architecture in terms of processing time and output integrity.
Data Scientist2020 - 2021Moneyhub
Technologies: Financial Data, Applied Mathematics, XGBoost, LightGBM, Time Series Analysis, Reporting, Data Visualization, Data Analysis, MongoDB, Automation, Amazon Athena, Databases, Git, Scikit-learn, NumPy, Business Intelligence (BI), Linux, Statistical Analysis, Algorithms, Data Science, Data Reporting, Pandas, Data Analytics, Amazon Web Services (AWS), Natural Language Processing (NLP), Machine Learning, Plotly, Jupyter Notebook, SQL, Amazon SageMaker, AWS, Python, Redshift, Big Data
- Implemented and productionized a personalized machine learning algorithm to classify transaction data using the AWS SageMaker and Lambda infrastructure.
- Detected trends in the customers' behavior and created frequent reports to present the results, which have been published regularly on the company's website.
- Completed various data analyses and POCs to answer requests from the business using a combination of SQL and Python for the back end and Jupyter Notebooks and Plotly to present findings.
Data Scientist2020 - 2020TradeDepot
Technologies: Amazon Web Services (AWS), Financial Data, Software Engineering, Loans & Lending, Credit Risk, Amazon SageMaker, AWS, Python, Redshift
- Analyzed the microloan data to identify the relevant features that have an impact on repayment behavior.
- Implemented and tested a Python module that returns a credit risk score together with a detailed explanation.
- Deployed that module on the AWS SageMaker and Lambda infrastructure to fully integrate it with the current system.
Data Scientist2019 - 2020Sopra Steria España
Technologies: Reporting, Data Visualization, Data Analysis, SQL Server Management Studio, Business Intelligence (BI), Pandas, Data Analytics, Microsoft Excel, Azure, Databricks, Python, SQL, Big Data, MySQL
- Developed new methods to measure business success for a retail client and its implementation in Python.
- Performed a post-analysis of retail promotions using SQL and Python and presented findings to stakeholders.
- Optimized SQL queries to extract insights from large tables.
- Restructured and optimized an internal Python package to extract and visualize statistics of large database tables.
Data Scientist (Master Thesis Student)2019 - 2019Allianz
Technologies: Applied Mathematics, Ggplot2, Data Analysis, Mathematics, SQL, Algorithms, Data Analytics, Plotly, Markdown, LaTeX, Python, R
- Wrote my thesis about machine learning methods to model life tables.
- Performed the preprocessing, analysis, and modeling of data with a size of over 100GB.
- Built and tested an R package for internal usage in the actuarial department.
- Conducted my final presentation in front of experts as part of the official training series.
- Implemented exhaustive performance optimization of R code using vectorization, parallelization, and optimized packages.
Data Scientist2018 - 2019Allianz
Technologies: Markdown, Microsoft PowerPoint, Natural Language Processing (NLP), XGBoost, LightGBM, Ggplot2, Financial Markets, Random Forests, Reporting, Dashboard Design, Data Visualization, Dashboard Development, Dashboards, Data Analysis, CSS, Databases, Business Intelligence (BI), Statistical Analysis, Data Science, Data Reporting, Machine Learning, Data Analytics, Microsoft Excel, Plotly, RStudio Shiny, SQL, Git, Python, RStudio, MySQL
- Implemented and supported extensive interactive data-driven dashboards in R-Shiny.
- Developed a product recommendation system for corporate clients based on the clients' characteristics and product history.
- Built a productive automated system for the early detection of problems with products or business processes based on client complaint data.
- Implemented the visualization of complex data and presentation of insights using Plotly, D3.js, and R Markdown.
- Performed topic modeling and text mining of client complaint texts using LDA.
- Constructed presentations concerning theory and programming packages within the field of machine learning.
- Created internal programming packages to streamline and simplify frequently used data science tasks.
Researcher2017 - 2018Fraunhofer Institute for Industrial Mathematics ITWM
Technologies: Financial Data, Applied Mathematics, Neural Networks, Time Series Analysis, Financial Markets, Optimization, Random Forests, Data Analysis, Keras, Scikit-learn, Statistical Analysis, Algorithms, Data Science, Machine Learning, Data Analytics, R, RStudio Shiny, PyTorch, Spyder, Anaconda, Linux, Python, RStudio
- Worked on the project Senrisk (Senrisk.eu/), which predicted price fluctuations of corporate and sovereign bonds based on news sentiments.
- Built recurrent neural networks in PyTorch to predict financial time series.
- Developed statistical methods for fraud detection in the health insurance industry.
- Implemented a Python package for financial time series prediction, including the integration to a web service.
- Constructed a software prototype in R Shiny to visualize the impact of different sample sizes in the context of fraud detection.
Intern2016 - 2017Universidad Técnica Federico Santa María
Technologies: Financial Markets, Data Analysis, Data Analytics, Microsoft Excel, R, C#
- Implemented software in C# to evaluate financial options based on the Black-Scholes model.
- Created a detailed report about the theoretical foundations of option price valuation.
- Conducted research related to the Black-Scholes model and financial time series.