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Nicolas N.NN

Nicolas N.

Data Scientist, python developer, mathematician

700 €/jour
1 projet
Genève, CH
3-7 ans

Délai de réponse moyen : 1h

À propos de Nicolas

Data scientist depuis 6 ans, j'ai effectué mon post doc en topological data analysis à l'EPFL (Lausanne) où j'ai travaillé sur l'étude de gros graphes du cerveau.
Je me suis ensuite mis à mon compte dans une entreprise de finance décentralisé, en tant que développeur python et data analyste.

J'ai une solide formation en mathématique et informatique.

  • Français

    Bilingue ou natif

  • Anglais

    Capacité professionnelle complète

En télétravail uniquement
Travaille majoritairement à distance

Expériences

  • CNRS (LEGOS)
    Refactorisation de code d'une application scientifique.
    septembre 2024 - octobre 2024 (2 mois)
    - Refactorisation de code python scientifique
    - Amélioration des performances
    - Conseil sur des eventuelles incohérences/erreurs dans la pipeline de data
    - Conseil en design/algorithme
    Python
  • Stake Capital Group
    Data Analyst
    août 2021 - mars 2023 (1 an et 7 mois)
    Blockchain data analyst:
    I was in charge of multiple and varied tasks mostly centered around our defi and gaming activity:

    - setting up our data stack, to be used for our frontend, and for analysis (a mix of many tools, mostly the graph)

    - analyzing blockchain data, user activity, player behavior
    - smart contract analysis (especially for investment purposes)


    Python developper:
    - automation in various domains (day to day activity, gamng, data retrieval)

    - adding functionality to some existing code base to interact with more dexes

    Some light experience with aws, react,typescript.

  • Ecole polytechnique fédérale de Lausanne
    Post Doc
    août 2017 - août 2021 (4 ans)
    Lausanne, Suisse
    I did a postdoc in a team of mathematicians working mainly with the blue brain project. It was a collaboration between computer neuroscientist and mathematician.


    My research mainly involve doing graph analysis, on big graph coming from the brain model of the rats. We used extensively TDA (topological data analysis) to compute (topological) graph invariant.

    My work mostly involved using a range of data analysis method and sometimes creating new ones to apply to our "big connectome graph".
    Base code was almost always python, with some c/c++ code binding when we needed fast computation.

    Here are a few activities I was involved with:
    - computing graph invariants (often on supercomputer via slurm)

    - creating variation of those invariants

    - applying known method to our data (ie reading neuroscience paper that analyse real data and doing the same on synthetic data to compare)
    - using a wide range of model depending on the data (from simple regression to random forest, not much DNN as they didnt really suited our use cases)

    Beside that I also had the usual teaching/mentoring, I taught linear algebra at EPFL mostly TD, supervising a few interns.


Avis

5,0

sur 1 évaluation

F

Fernando

CNRS (LEGOS)

Avis laissé le 05/11/2024

Compétent, réactif et très à l'écoute, avec une vraie envie de rendre service.

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Formations

  • Phd in computer science
    Université Paris Saclay
    2016
    I worked on topological models of concurency in the LIX laboratory at the école Polytechnique.
  • Agrégation de Mathématique
    Paris Saclay
    2012
    French national examination to be able to teach math.

Compétences (12)

Catégories