Francesco Fontan, Data Scientist and Developer in Berlin, Germany
Francesco Fontan

Data Scientist and Developer in Berlin, Germany

Member since January 30, 2023
Francesco is a seasoned data scientist with robust analytical and technical capabilities. His area of interest is tackling business problems using traditional machine learning and deep learning for computer vision or natural language processing (NLP) tasks. Fascinated by operational research, optimization, and GPU-accelerated computing, Francesco has a strong machine learning and cloud engineering background that helps him drive conversations and coordinate heterogeneous teams.
Francesco is now available for hire




Berlin, Germany



Preferred Environment

Linux, Python 3, Pandas, Scikit-learn, TensorFlow, PyTorch, Hugging Face

The most amazing...

...thing I've developed is a scheduling tool for a cruise company using forecasting and optimization, saving $2 million per year.


  • Data Scientist

    2022 - PRESENT
    Levi Strauss & Co.
    • Led the technical global promotion team, providing recommendations for five global markets for retail stores, outlets, and eCommerce and creating value of around $20 million in additional revenue per year.
    • Redesigned the pricing recommendation tool achieving an increase in speed by six times using BigQuery and Apache Airflow.
    • Supported the migration from AWS to Google Cloud Platform (GCP), coordinating the work between data engineers and machine learning (ML) engineers.
    Technologies: Data Science, Optimization, Pricing, Promotion
  • Data Scientist

    2022 - 2022
    Delivery Hero
    • Designed the first version of the picker scheduling tool that optimized the shifts of the people working in dark stores leveraging Python and mixed-integer programming (MIP), reducing the costs by more than $1 million per year.
    • Prototyped the first version of a smart location-based inventory that suggests where to place items optimally to minimize the picking time and other operational activities inside a warehouse.
    • Created automated pipelines for autoformatting using Python and SQL codes based on custom rules, helping data scientists to speed up deploys to two hours per person per sprint and to bring uniformity across different teams.
    Technologies: Python, Optimization, Data Science
  • Data Scientist

    2017 - 2021
    Machine Learning Reply
    • Created text classification to analyze emails automatically, speeding up the entire business process by 10 times.
    • Designed an optimization tool for a cruise company that handles embarking and disembarking for more than 50,000 people, resulting in estimated average savings of $1 million per year.
    • Conducted 10 lectures for the course "AI and ML: Platforms and vendor solutions" to graduate students enrolled in the second-level master studies in artificial intelligence and cloud at the Polytechnic University of Turin.
    • Worked on a recommender system for Reply's internal social network using traditional collaborative filtering methods, item-based models, and NLP techniques. The algorithms handled over 10 thousand active daily users and increased engagement by 10%.
    • Redesigned an ML model for swaption prices, achieving better performance by decreasing the mean squared error (MSE) by 10% compared to the previous implementation and lowering the RAM required by 30%, with an increased speed by 1.5 times.
    • Built a system to detect and classify various road defects, as predictive maintenance applied to highway asphalt is crucial to cut costs. This object detection model was based on YOLOv5.
    Technologies: Python, PyTorch, Deep Learning, Machine Learning, Forecasting, Computer Vision, Natural Language Processing (NLP), Categorization, Regression, Google Cloud Platform (GCP), Unsupervised Learning


  • NLP Ticketing System

    I led a team of three people to design a machine learning tool on Microsoft Azure to improve the ticketing management of a telco customer.

    This system analyzed emails received from customer support and logs from the network infrastructure, extracting useful information using NLP techniques to open, assign, and finally close tickets fully automatically.

    This automation increased the speed by 10 times, predicting more than ten ticket fields with an average accuracy of 90%. Finally, the pipeline could scale: training was performed on half a million records, while the inference module handled more than 20 tickets per hour.

  • Smart Planning for a Cruise Line

    The main goal was to use artificial intelligence to enhance the planning phase of crew members by optimizing the global fleet planning by minimizing the money spent on flight tickets without lowering the expected quality.

    I designed a Python tool based on Google OR-Tools, an optimization suite by Google AI, able to solve MIP problems that involved more than 30,000 embarks every year, obtaining an estimated average saving of 10%, corresponding to $1 million per year.


  • Languages

    Python 3, R, Python, C++
  • Libraries/APIs

    Pandas, Scikit-learn, TensorFlow, PyTorch
  • Paradigms

    Data Science, DevOps
  • Platforms

    Google Cloud Platform (GCP), Linux
  • Other

    Statistics, Probability Theory, Deep Learning, Machine Learning, Optimization, Natural Language Processing (NLP), Computer Vision, Machine Learning Operations (MLOps), Artificial Intelligence (AI), Hugging Face, Network Science, Pricing, Promotion, Cloud, Data Engineering, GPU Computing, Text Classification, Forecasting, Reinforcement Learning, Categorization, Regression, Unsupervised Learning
  • Tools



  • Master's Degree in Mathematical Engineering
    2015 - 2017
    Polytechnic University of Turin - Turin, Italy
  • Bachelor's Degree in Mathematics and Computer Science
    2012 - 2015
    Polytechnic University of Turin - Turin, Italy


  • TensorFlow Developer Certificate
    JUNE 2022 - PRESENT
  • Professional Machine Learning Engineer
    Google Cloud
  • Professional Data Engineer
    Google Cloud
  • Deep Learning Institute Certified Instructor
  • Microsoft DAT257x: Reinforcement Learning Explained
    JULY 2018 - PRESENT
  • Deep Learning
    MARCH 2018 - PRESENT

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