Daniel Nouri, Software Developer in Berlin, Germany
Daniel Nouri

Software Developer in Berlin, Germany

Member since December 4, 2018
Daniel is a machine learning specialist with a focus on deep learning, a software engineer with over 18 years of experience in building reliable, high-performing systems, and the owner of Natural Vision UG, based in Berlin, Germany. An exceptional communicator and self-starter who's contributed to a number of projects over the years (including Scikit-learn), Daniel joined Toptal to find work that piques his interest.
Daniel is now available for hire



  • Python, 18 years
  • Automated Testing, 18 years
  • Git, 10 years
  • Pandas, 8 years
  • Scikit-learn, 8 years
  • NumPy, 8 years
  • PyTorch, 3 years


Berlin, Germany



Preferred Environment

Ubuntu, Git, Emacs

The most amazing...

...thing I've done was when I led a team of researchers/engineers to improve on a state-of-the-art app with real-time medical image analysis.


  • Machine Learning Scientist | Consultant | Trainer

    2014 - PRESENT
    Natural Vision UG
    • Developed and deployed a predictive analytics system for parcel delivery for a Fortune Global 500 company.
    • Developed a predictive analytics system for sales forecasts which outperforms human analysts in terms of speed and accuracy for a Fortune Global 2000 company.
    • Developed a deep-learning-based bioacoustics recognition system for detection and classification of marine mammals, in collaboration with Oregon State University.
    • Helped Jetpac, a company that built city guides using artificial intelligence, implement a deep-learning-based computer vision system that finds objects inside millions of Instagram photos. Jetpac then uses the information extracted from images to automatically categorize locations.
    Technologies: Deep Learning, Python, Scikit-learn
  • Lecturer

    2014 - 2018
    Data Science Retreat
    • Regularly gave courses around Python, Scientific Python, machine learning, and deep learning.
    • Developed course material and hands-on tutorials.
    • Advised students on portfolio projects and career choices.
    Technologies: Python, Machine Learning
  • Chief Scientist

    2016 - 2017
    Samsung Research America (Samsung NEXT)
    • Researched and developed algorithms for the estimation of liquid flow inside of water pipes, using an IoT sensor that is attached to the outside of the pipe.
    • Implemented signal processing and machine learning algorithms for lower-power, embedded devices.
    • Built a rig for the semi-automatic acquisition of labeled training data for water flow detection; using Raspberry Pi, an inline flow meter for ground truth, and several sensors.
    • Developed low-power, digital wireless communication protocols. Built a system for remote maintenance of sensors and communication hubs.
    Technologies: Python, Microcontrollers, Machine Learning, C/C++, IoT
  • Head of Machine Learning

    2015 - 2016
    4Catalyzer, Butterfly Network
    • Led a team of data scientists in solving problems in medical image analysis.
    • Implemented real-time, state-of-the-art computer vision applications for ultrasounds.
    • Managed the acquisition of medical imaging data and worked with medical experts to label data.
    • Researched and developed partially novel applications for ultrasound and MRI.
    Technologies: Python, Medical Imaging



  • Languages

    Python, SQL, JavaScript
  • Libraries/APIs

    PyTorch, Scikit-learn, TensorFlow, Pandas, NumPy
  • Tools

    Git, Emacs, Pytest
  • Paradigms

    Automated Testing, Pair Programming, Agile
  • Platforms

    Linux, Azure, Amazon Web Services (AWS), Google Cloud Platform (GCP)
  • Other

    Debugging, Pytorch
  • Storage

  • Frameworks

  • Neural Networks for Machine Learning
    University of Toronto via Coursera
  • Machine Learning
    Stanford University via Coursera

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