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Danut M.

Machine Learning Engineer

Danut is a Machine Learning Engineer with five years of experience. His role involves designing, implementing, and maintaining machine learning systems.

He leverages his knowledge in computer science, statistics, and data analysis to develop algorithms and models capable of learning and making predictions or decisions from extensive datasets.

Danut collects and preprocesess data, ensuring its quality and compatibility with the chosen algorithms. He selects and configures machine-learning frameworks, libraries, and tools to construct robust and scalable models. In addition, he conducts experiments to train and fine-tune machine learning models, consistently iterating on their performance and optimizing them for various criteria, including accuracy, speed, and other desired outcomes.

Main expertise
  • Data Science 5 years
  • Machine Learning
    Machine Learning 5 years
  • NumPy
    NumPy 5 years
Other skills
  • Keras
    Keras 3 years
  • PyTorch 3 years
  • TensorFlow
    TensorFlow 3 years
Danut M.


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Selected experience


  • Machine Learning Engineer

    Deutsche Bank - 2 years 2 months

    • Benchmarked and augmented different recommendation algorithms on financial data using Numpy, Pandas, and scikit-learn
    • Designed and implemented a PySpark pipeline for daily predictions, including logging and monitoring components.


    • Technologies:
    • Machine Learning Machine Learning
    • NumPy NumPy
    • Pandas Pandas
    • Scikit-learn Scikit-learn
  • Course Instructor

    Bittnet Training - 2 years 5 months

    • Delivered courses on Machine Learning concepts, covering Python libraries such as NumPy, Pandas, Keras, Matplotlib, and Seaborn.
    • Delivered courses on DevOps concepts, covering technologies like Kubernetes, Docker, Terraform, and Ansible.


    • Technologies:
    • Keras Keras
    • NumPy NumPy
    • Pandas Pandas
    • Python Python
  • Researcher

    CRC MINES - 1 year 8 months

    • Created an LSTM model in Keras to detect anomalous varnish phenomena by predicting the expected time evolution of air compressor parameters.
    • Utilized OpenPose and OpenCV to design a robust, automated repetition counting system for assessing squats performed by athletes.
    • Developed various solutions for CT and PET scans, including 2D image registration, 3D point cloud registration, brain segmentation, blob detection, image fusion, and 3D visualization. This was achieved using Numpy, OpenCV, and Scikit-image.


    • Technologies:
    • Machine Learning Machine Learning
    • NumPy NumPy
    • Scikit-learn Scikit-learn


  • MSc.Computer and Network Security

    Polytehnic University of Bucharest · 2020 - 2022

  • BSc.Computer Engineering

    Polytehnic University of Bucharest · 2016 - 2020

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