Daniel Sánchez Santolaya

Data Scientist at BBVA AI Factory
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Location
Barcelona, Catalonia, Spain, ES

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Pavan Kumar C.

Daniel and I worked together at Pegasystems Amsterdam. Daniel consistently impressed me with his ability to quickly grasp complex concepts and adapt to new technologies. His strong analytical skills and in-depth understanding of machine learning algorithms were instrumental in the success of several projects we collaborated on. Notably, he integrated RNN (Recurrent Neural Networks) seamlessly into our product. He played a pivotal role in creating the NextBestMoment demo, which received resounding applause at PegaWorld. Beyond his technical capabilities, Daniel is a remarkable person. He is open-hearted, down-to-earth, and consistently displays empathy towards his colleagues and peers. He is quick to lend a helping hand and foster a collaborative work environment. I have no doubt that Daniel would be an exceptional asset to any organization fortunate enough to have him on their team. I wholeheartedly recommend and look forward to witnessing his continued success in the field of machine learning and beyond.

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Credentials

  • Machine Learning
    Coursera
    May, 2014
    - Sep, 2024

Experience

    • Spain
    • Banking
    • 1 - 100 Employee
    • Data Scientist
      • Jul 2019 - Present
    • United States
    • Software Development
    • 700 & Above Employee
    • Software Engineer - Data Science
      • Sep 2017 - Jun 2019
    • Research Intern
      • Feb 2017 - Jul 2017

      Working on the Master Thesis " Using recurrent neural networks to predict customer behavior from interaction data" Description: User behavior can be represented as sequential data describing the interactions of the user through the time. Examples of these interactions are items that the user purchases or views. Recurrent Neural Networks have been very effective for learning complex sequential patterns in Natural Language Processing and Computer Vision. In this master thesis we try to apply RNN to model and predict customer behavior from this interaction data. The proposed approach can be seen as another method of collaborative filtering using the temporal information, where RNN are used to predict which items the user will consume considering the items that the user consumed in the past. In our goals we want to explore how effective are RNNs in this scenario. We study how item embeddings and attentional mechanism can help to improve performance and explain predictions. Show less

    • Spain
    • Information Technology and Services
    • 1 - 100 Employee
    • Software developer
      • Jan 2014 - Aug 2015

      Worked in Cuatrecasas Gonçalves Pereira client. Analysis and development of .Net applications. Worked in Cuatrecasas Gonçalves Pereira client. Analysis and development of .Net applications.

    • Belgium
    • Legal Services
    • 1 - 100 Employee
    • Software developer
      • Jun 2013 - Dec 2013

      Analysis and development of .Net applications.

    • Intern
      • Oct 2012 - Jun 2013

Education

  • Universiteit van Amsterdam
    MSc Artificial Intelligence
    2015 - 2017
  • Universitat Autònoma de Barcelona
    Ingeniería Informática
    2007 - 2013

Community

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