Dávid Natingga, Ph.D., Data Scientist and Developer in Žilina, Žilina Region, Slovakia
Dávid Natingga, Ph.D.

Data Scientist and Developer in Žilina, Žilina Region, Slovakia

Member since October 13, 2020
Dávid helps his clients solve the most complex analytical problems using mathematics, data science, and technology. He has aided numerous clients, including big names like Infosys, Palantir, Suez, and TomTom. Dávid's top niche is high-frequency time series analysis. He makes the impossible possible. His inventions encompass robust Fourier transform, robust anomaly detection, and overcoming the curse of higher dimensionality. Dávid's strengths include curiosity, creativity, and persistence.
Dávid is now available for hire


  • Freelance
    Research, GIS, Time Series Analysis, Anomaly Detection, Forecasting...
  • TomTom
    GIS, Python, Bash, Git, Amazon Web Services (AWS), Java, Agile
  • Pact Coffee (Intern)
    Git, Recommendation Systems, Algorithms, Machine Learning, Go, Data Science...



Žilina, Žilina Region, Slovakia



Preferred Environment

Amazon Web Services (AWS), SQL, Go, Julia, Python, TensorFlow, Git, Linux, XGBoost

The most amazing...

...algorithm optimization I performed was to replace a heavy stack of AWS instances with one mathematical formula, which computed an instant on an ordinary laptop.


  • Data Scientist

    2019 - PRESENT
    • Created novel machine learning algorithms for transient detection, localization, anomaly detection, and demand forecasting, within hydraulic networks.
    • Contributed to a patented event-detection solution for the hydraulic time series.
    • Designed and developed analytical solutions to tackle specific problems faced by water utility companies.
    • Created data validation algorithms to validate imported data and to detect and correct corrupted time-series data.
    • Built an automatic report generation tool to provide an overview of the quality of the data, pinpoint specific problems in the data, and detect problems with the devices captured by the data.
    • Designed a scalable SQL database structure, based on the TimescaleDB extension, to run research experiments at scale.
    • Found bugs in the client's infrastructure and advised its simplification and decoupling to make it more robust and more efficient for working between development and data science teams.
    • Created novel algorithms to detect mechanical misconfiguration of the sensor devices and correct the data from the misconfigured devices.
    • Conducted an extensive statistical study on the uncertainty and confidence intervals of the data received from the monitoring devices.
    • Developed a strategic roadmap and new architecture for advanced analytics.
    Technologies: Research, GIS, Time Series Analysis, Anomaly Detection, Forecasting, Convex Optimization, Linear Programming, Statistics, Mathematics, Optimization, Algorithms, Matplotlib, Plotly, Keras, TensorFlow, Scikit-learn, Pandas, NumPy, Julia, Python, Data Science, Amazon Web Services (AWS), Predictive Analytics, Git, GitLab, GitHub, JSON, JSON API, Mathematical Analysis, Mathematical Modeling, Data Analytics, Data Analysis, Rust
  • Software Engineer

    2017 - 2018
    • Figured out the specification of complex legacy GIS data for which no specification was known and migrated it successfully to a new format.
    • Converted, processed, and generated the entire world map data used in navigation platforms around the globe.
    • Developed probabilistic map data error detection tools for the given imperfect and erroneous data.
    Technologies: GIS, Python, Bash, Git, Amazon Web Services (AWS), Java, Agile
  • Data Scientist

    2014 - 2014
    Pact Coffee (Intern)
    • Developed an algorithm in Go for recommending new coffees, with no user feedback, based on their intrinsic properties.
    • Achieved the algorithmic performance superior to a professional coffee connoisseur.
    • Delivered fast execution speeds on ordinary hardware.
    Technologies: Git, Recommendation Systems, Algorithms, Machine Learning, Go, Data Science, Data Analytics, Data Analysis
  • Forward Deployed Engineer

    2013 - 2013
    Palantir Technologies (Intern)
    • Developed a document similarity search plugin for the Palantir Government platform with an integrated security layer for the Elasticsearch server, covering https and the Palantir internal authentication endpoint and access control list.
    • Extended the codebase, which comprised over one million lines of code, had limited documentation, and depended on the legacy software.
    • Updated the previously developed plugin, HTML-Exporter for Maps, thus satisfying existing clients.
    Technologies: Java
  • Forward Deployed Engineer

    2012 - 2012
    Palantir Technologies (Intern)
    • Developed a Java plugin, HTML-Exporter for Maps, exporting tiles, Palantir objects, KML objects, layers, and other map data from the Palantir Government platform to an HTML file.
    • Transformed tiles between incompatible geographic information systems.
    • Communicated directly with the clients, achieving a track record of successful deployments and uses of the plugin I developed.
    Technologies: Algorithms, GIS, Java
  • Instep Research Intern

    2011 - 2011
    Infosys Labs
    • Researched the optimization of parallel algorithms and analyzed asymmetric workload distribution, resulting in a publication.
    • Implemented a home-grown sequence data mining algorithm on Nvidia (CUDA) graphic cards.
    • Developed auxiliary tools, including a statistical sequence generator.
    Technologies: Git, Research, Optimization, Statistics, CUDA, C++, C, Data Science


  • Find Optimal Battery Size

    Using linear optimization (Google OR-tools), found an optimal battery size based on the battery price, charge rate, capacity, and the opportunities with which the battery can be monetized, including energy trading, balancing, and storage.

  • Non-intrusive Load Monitoring

    Turned a meter into a smart meter using its raw current and voltage data to track devices for consumption and predictive maintenance. Challenged state-of-the-art research and invented new robust algorithms and techniques to reach near-perfect performance for device disaggregation (accuracy over 99%).

  • Smart Electric Vehicle Charging

    Charging electric vehicles (EVs) may be more involved than one thinks. Each electric vehicle has a different charging priority, has a different minimum and maximum charging current, and uses different phases. These phases may be shuffled while the EV charging station's grid connection has limitations, to name a few of the many caveats.

    David has developed and successfully deployed a smart EV charging algorithm that takes care of the whole process using clever mathematics, optimization techniques, and algorithms with heuristics.

  • Cat, Dog, or Panda?

    A deep convolutional neural network to detect and classify images of animals into three categories: cats, dogs, and pandas. I created the model, which was an adaptation of a general pre-trained VGG16 model to offset the limited amount of training data available. The model classification accuracy on the validation data was over 97%.

  • Math Glass

    The Math Glass puzzle for Android phones uses simple arithmetic operations to provide an intellectual challenge at various difficulty levels. Primary school students can solve the initial levels. At later levels, one discovers that the human brain is too limited to test all the possible combinations to find the solution; this is where mathematical ingenuity is required to find shortcuts to success. In the end, a diligent player will discover basic tricks of number theory.

  • Sophia Number Guesser

    A simple Android puzzle accompanied by a character named Sophia. She is a baby mathematician who uses the beauty of mathematics at various levels of difficulty to find out the hidden digit in the number you are thinking of.

    Can you figure out how Sophia can guess the hidden digit?


  • Tools

    Git, GIS, Plotly, GitLab, GitHub
  • Paradigms

    Anomaly Detection, Data Science, Linear Programming, Agile
  • Platforms

    Linux, CUDA, Amazon Web Services (AWS)
  • Other

    Mathematics, Algorithms, Machine Learning, Artificial Intelligence (AI), Statistics, Optimization, Research, Recommendation Systems, Forecasting, Time Series Analysis, Number Theory, Data Analysis, Data Quality Analysis, Data Analytics, Data Reporting, Mathematical Analysis, Mathematical Modeling, Convex Optimization, Predictive Analytics, Data Visualization, Google Cloud ML, Deep Learning, Object Recognition, Computer Vision, Computer Vision Algorithms, Deep Neural Networks, NLU, Natural Language Understanding (NLU), Big Data, Economics, Cost Reduction & Optimization, Linear Optimization, OR-Tools, Combinatorial Optimization, Signal Processing
  • Languages

    Python, Julia, Go, Java, SQL, C, C++, Bash, JavaScript, Python 3, Rust
  • Libraries/APIs

    TensorFlow, NumPy, Pandas, Scikit-learn, Keras, Matplotlib, Node.js, JSON API, XGBoost
  • Storage



  • Ph.D. in Mathematics (Computability Theory)
    2014 - 2019
    University of Leeds - Leeds, England, United Kingdom
  • Master of Engineering Degree in Computing (Artificial Intelligence)
    2010 - 2014
    Imperial College London - London, United Kingdom


  • Problem Solving
    JUNE 2020 - PRESENT
    Online Freelance Platform

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