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Luca Massimo

data scientist

Trabaja a distancia desde Madrid

  • 40.4167
  • -3.7036
Proponer un proyecto El proyecto sólo comenzará si aceptas el presupuesto de Luca.
Proponer un proyecto El proyecto sólo comenzará si aceptas el presupuesto de Luca.

Localización y desplazamiento

Madrid, España
Trabajo a distancia
Lleva a cabo sus proyectos principalmente en remoto


Duración del proyecto
  • ≤ 1 semana
  • ≤ 1 mes
  • Entre 1 y 3 meses
  • Entre 3 y 6 meses
  • ≥ 6 meses




Github : DonBeo DonBeo
  • 0 Seguidores
  • 8 Repositorios
  • 0 Gists

Stack Overflow

Stack Overflow : Donbeo Donbeo
  • 14447 Reputación
  • 163 Bronce
  • 94 Plata
  • 30 Oro



Habilidades (16)

Luca en pocas palabras

Luca Puggini is a dynamic person and a fast learner with a strong interest in quantitative and computational subjects. He has a deep knowledge of machine learning, artificial intelligence, mathematics and statistics and a wide experience in computational intelligence techniques applied to large datasets. He is also passionate about programming, and experienced in several programming languages. In particular he has an advanced knowledge of Python and its numerical libraries (tensorflow, sklearn, numpy, scipy, pandas, statsmodels) and a working knowledge of R, Scala, C and Matlab.


Corvil / Pico Trading

High tech

Data Scientist

Madrid, España

octubre de 2016 - Hoy

Development of algorithmic solutions to monitor network efficiency in high frequency trading platforms.
Main achievements:

Development algorithms for anomaly detection (distribution agnostic) in time series at scale
(monitoring up to 50k metrics).

Analysis of diagnostics data for common product root cause detection (customer support)

Development of pipelines for high volume data collection.


High tech

Data Scientist (Visiting Researcher)

Dublin City pre 1849, Irlanda

diciembre de 2013 - septiembre de 2016

Researcher in the Data Analytics department at Intel. Development of systems for efficient real time
data analysis in a semiconductor manufacturing contest.

Main achievements:

Development of anomaly detection systems for wafer processing.

Predictive model for high dimensional Optical Emission Spectroscopy data.

Unsupervised features selection algorithm for robust and interpretable dimensionality reduction.

Peak calibration system through the use of genetic algorithms

Google - Alphabet/Google

High tech

Developer (Student Developer)

Dublín, Irlanda

mayo de 2015 - agosto de 2015

Funded by Google as part of the GSoC 2015 project to develop a Generalized Additive Models toolbox
for the python statsmodels library. The project was under the supervision of Josef Perktold from McGill
University. The code and the library are available at

- Development of a GAM toolbox for python. A contributions of around 2300 lines of python code.

Bergen University

Sector médico

Statistician 20%

Bergen, Noruega

enero de 2014 - noviembre de 2016

2 recomendaciones externas

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