Nicolas Keller, Data Scientist and Developer in Berlin, Germany
Nicolas Keller

Data Scientist and Developer in Berlin, Germany

Member since January 21, 2020
With a strong mathematical background (a master's degree in mathematics), Nicolas is a passionate data scientist who can contribute the ideal combination of machine learning knowledge, practical programming skills, and a problem solving and analytical mindset to a project. He has a demonstrated history of transforming business problems into data-driven solutions and recently has worked as a data scientist at the global insurance company, Allianz.
Nicolas is now available for hire


  • Self-employed
    Jupyter Notebook, PyCharm, HTML, Beautiful Soup, Sublime Text...
  • Focus Sensors Limited
    Python, Algorithms, Apache Kafka, SciPy, Testing, Streaming Data...
  • Moneyhub
    Financial Data, Applied Mathematics, XGBoost, LightGBM, Time Series Analysis...



Berlin, Germany



Preferred Environment

Jupyter Notebook, RStudio, Git, Linux

The most amazing...

...thing I've coded was an R package to predict life insurance claims based on individual characteristics. It is a novel approach going beyond the status quo.


  • Data Scientist

    2019 - PRESENT
    • 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.
    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
  • Data Scientist

    2020 - 2021
    Focus Sensors Limited
    • 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.
    Technologies: Python, Algorithms, Apache Kafka, SciPy, Testing, Streaming Data, Signal Processing, Docker
  • Data Scientist

    2020 - 2021
    • 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.
    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
  • Data Scientist

    2020 - 2020
    • 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.
    Technologies: Amazon Web Services (AWS), Financial Data, Software Engineering, Loans & Lending, Credit Risk, Amazon SageMaker, AWS, Python, Redshift
  • Data Scientist

    2019 - 2020
    Sopra Steria España
    • 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.
    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
  • Data Scientist (Master Thesis Student)

    2019 - 2019
    • 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.
    Technologies: Applied Mathematics, Ggplot2, Data Analysis, Mathematics, SQL, Algorithms, Data Analytics, Plotly, Markdown, LaTeX, Python, R
  • Data Scientist

    2018 - 2019
    • 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.
    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
  • Researcher

    2017 - 2018
    Fraunhofer Institute for Industrial Mathematics ITWM
    • Worked on the project Senrisk (, 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.
    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
  • Intern

    2016 - 2017
    Universidad Técnica Federico Santa María
    • 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.
    Technologies: Financial Markets, Data Analysis, Data Analytics, Microsoft Excel, R, C#


  • EU Project SENRISK

    As a member of the Fraunhofer ITWM research institute, I participated in the EU-funded SENSIRK project. The main goal of this project was to predict corporate and sovereign bond prices based on news sentiments.

    My part was mainly the implementation of the prediction system. We used recurrent neural networks and boosting methods and built a Python package to streamline the whole process.

  • Analysis and Visualization of WhatApp Chats

    A Python toolkit to visualize WhatsApp chats. It offers some fun visualizations of single or group chats. Additionally, it has an interactive dashboard that can be used to navigate through visualizations. It transforms the original text file into a handy data frame and handles different input formats, including other iOS and Android versions.

  • Machine Learning Demonstration Tool

    This shiny app serves as a little user interface to demonstrate some standard machine learning tasks. You can upload an example data set and edit, visualize and model it. I used it for demonstration purposes, especially when showing the basic ML concepts to non-technical users.

  • Android App Course Analyzer

    An Android application to track given courses. It is used by a small group of persons for a specific business use case. You can insert a course and specify received evaluations.

    On the main page, you have an overview of all courses, and you can export and import a list of the courses. Finally, you can see statistics of the evaluations, and a map shows the places where the courses have taken place with some additional information about it. Currently, it is only available in Spanish.


  • Languages

    Python, R, SQL, C#, Markdown, Kotlin, HTML, CSS
  • Frameworks

    RStudio Shiny, LightGBM, Selenium
  • Libraries/APIs

    Pandas, Ggplot2, XGBoost, Keras, Scikit-learn, Beautiful Soup, NumPy, PySpark, PyTorch, SciPy
  • Tools

    Plotly, Amazon SageMaker, Amazon Athena, Git, LaTeX, Sublime Text, Spyder, Microsoft Excel, Amazon QuickSight, PyCharm, Microsoft PowerPoint
  • Paradigms

    Data Science, Business Intelligence (BI), Automation, Testing
  • Platforms

    RStudio, Amazon Web Services (AWS), Linux, Azure, Jupyter Notebook, Anaconda, Databricks, Apache Kafka, Docker
  • Other

    Data Analysis, Dashboards, Data Analytics, Applied Mathematics, Mathematics, Dashboard Development, Data Visualization, Machine Learning, Random Forests, Dashboard Design, Reporting, Data Reporting, AWS, Financial Markets, Financial Data, Big Data, Algorithms, Process Automation, Optimization, Web Scraping, Web Crawlers, Time Series Analysis, Statistical Analysis, Neural Networks, Credit Risk, Loans & Lending, Software Engineering, Data Engineering, Android Development, Natural Language Processing (NLP), Streaming Data, Signal Processing
  • Storage

    Redshift, Databases, SQL Server Management Studio, MySQL, MongoDB


  • Master of Science Degree in Financial and Actuarial Mathematics
    2016 - 2019
    Technical University Kaiserslautern - Kaiserslautern, Germany
  • Spent an Exchange Year in Financial Mathematics
    2016 - 2016
    Universidad Técnica Federico Santa María - Valparaíso, Chile
  • Bachelor of Science Degree in Mathematics
    2013 - 2016
    Technical University Kaiserslautern - Kaiserslautern, Germany


  • Big Data Fundamentals with PySpark
  • Applying SQL to Real-world Problems

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