Andrea Nalon, Data Scientist and Python Developer in Mestre, Italy
Andrea Nalon

Data Scientist and Python Developer in Mestre, Italy

Member since January 19, 2016
Andrea is a data scientist with a great deal of experience in programming with R, Python, VBA, Excel, SQL, and about four years as a quantitative analyst/trader. In addition to a master's degree in engineering, he has several certifications in quantitative analysis, machine learning as well as computational finance. His strong points are data analysis in order to find out predictive patterns with mathematical and statistical analysis.
Andrea is now available for hire


  • Osprey Underwriters
    JSON, Moodle, SQL, SQLAlchemy, Pandas, Python, MySQL, AWS, DigitalOcean...
  • Avepa
    Microsoft Access, Microsoft Excel, Python, R, PL/SQL, Oracle, Algorithms...
  • SPP (via Toptal)
    Visual Basic for Applications (VBA), Microsoft Excel, NumPy, Pandas, Python...



Mestre, Italy



Preferred Environment

Subversion (SVN), Git, Anaconda, RStudio, Linux, MacOS, Windows

The most amazing...

...thing I've created is a successful trading strategy that fits with machine learning techniques across different futures contracts CME (ES, YM, CL, GC, & more).


  • Data Scientist

    2017 - PRESENT
    Osprey Underwriters
    • Created a back-end system and implemented various algorithms to compute the insurance premiums of different products.
    • Handled the data cleaning and the entire design of the database architecture across several DB schemas, both manually and via the programming of many Python scripts and Jupyter notebooks.
    • Integrated and cooperated on automated tasks running between different servers as required by the customer.
    • Customized an already installed and running Moodle server used for video courses–enabling the authentication of users on an external MySQL database with various levels of control.
    • Implemented a number of MySQL stored procedures—using JSON strings as a list of parameters to be transferred to the database—for improved, easier integration between the back end and front end of various web apps.
    • Managed the migration of a MySQL production database from a provider (Compose) to another (DigitalOcean) by also upgrading its major version from 5 to 8.
    • Created a testing MySQL database on Amazon AWS infrastructure using their RDS service.
    • Managed a small team of two developers, coordinating their front-end development work to integrate it with the code and database I've created in the back end. I also owned their technical interviews during the hiring process.
    Technologies: JSON, Moodle, SQL, SQLAlchemy, Pandas, Python, MySQL, AWS, DigitalOcean, Amazon Web Services (AWS), Linux, Linux CentOS 7, Back-end
  • Data Analyst

    2011 - PRESENT
    • Implemented an automatic report generator in order to produce statistics reports for the European Commission with detailed data of payments.
    • Created software to replicate the legacy software used to calculate payments and check the integrity of an internal calculus algorithm.
    • Wrote several views and queries to an Oracle back-end database to retrieve payments information.
    • Created R scripts to sync a Pentaho repository with a Oracle database in order to align metadata and descriptions of every Pentaho report.
    • Developed a repository with specifications of Oracle views through interviews to different stakeholders.
    Technologies: Microsoft Access, Microsoft Excel, Python, R, PL/SQL, Oracle, Algorithms, Pandas, SQL, Oracle PL/SQL, Windows, Data Engineering, Back-end
  • Financial Model Builder

    2016 - 2016
    SPP (via Toptal)
    • Migrated all of the features and elaboration tasks of a complex financial model built with several huge Excel workbooks into 2 Python scripts written to speed up all computation—from more than 10 hours down to a few minutes. Both scripts also implement a quality check of the input data and have constraints to assure reliable and robust computation.
    Technologies: Visual Basic for Applications (VBA), Microsoft Excel, NumPy, Pandas, Python, Algorithms, Data Science, Data Engineering, Back-end
  • Quantitative Analyst/Trader

    2015 - 2015
    Glory Trading Systems GmbH
    • Developed algorithmic trading strategies.
    • Performed time series analysis (OHLC and tick data).
    • Implemented statistical analysis, linear regression, and machine learning.
    • Developed code in R and Python.
    Technologies: Statistics, Algorithmic Trading, Machine Learning, Pandas, NumPy, Python, R, Trading, Algorithmic Trading Analysis, Algorithms, Data Science, RStudio, Data Engineering, Back-end
  • Data Analyst

    2010 - 2010
    City of Treviso
    • Created a system for the employment center in order to statistically analyze labor market data and dynamically create reports with KPI data required by the client.
    • Wrote reports with VBA programming in order to dynamically gather data from the back-end MySQL database.
    • Created stored procedures and views in the MySQL back-end database in order to calculate and filter out any unnecessary data.
    Technologies: Key Performance Indicators (KPIs), Statistics, Visual Basic for Applications (VBA), Microsoft Excel, MySQL, Algorithms, Microsoft Access, SQL, Data Science, Windows, Data Engineering, Back-end
  • Business Analyst

    2007 - 2010
    GN ReSound
    • Created a data warehouse reporting solution in order to monitor finance, sales, and production departments.The system was connected to the back-end ERP (Navision) in order to gather data, and dynamically create several Excel reports, and a Microsoft Access database to interact with.
    • Wrote Excel reports that had a dynamical update feature where data was downloaded from a back-end database and then the cells inside the sheet were filled in and elaborated as required by the client.
    • Backed up some data from SQL Server into a local Microsoft Access database for more complex analysis and also to let the client choose from different filters and aggregation of sales; he could also print reports of his queries.
    • Developed software that contained a real-time calendar with upcoming orders and display of some KPI indicators to monitor the production process of hearing instruments.
    Technologies: Microsoft Excel, Visual Basic for Applications (VBA), Microsoft SQL Server, Algorithms, Microsoft Access, SQL, Windows, Visual Basic, Data Engineering


  • A Simple Quantitative Approach of the Three-Bar Reversal Pattern

    As many discretionary traders know, the Three-Bar Reversal pattern is known as a good pattern to enter a trade with a high-win rate. I've tried to study that pattern with the S&P 500 index in order to see if it is reliable, and I've studied a modified version that's more reliable.

  • Machine Learning Applied to Human Activity Recognition

    The goal of this research is to explore a data set of recorded values from life log systems for monitoring energy expenditure and for supporting weight-loss programs, and digital assistants for weight-lifting exercises.

  • Coursera Certificates

    Received a certificate in the following Coursera courses:
    1) University of Washington
    -Machine Learning: Classification (2016)
    -Machine Learning: Regression (2016)
    -Machine Learning Foundations: A Case Study Approach (2015-2016)

    2) University of Michigan
    -Programming for Everybody: Python (2015)

    3) Johns Hopkins University
    -Practical Machine Learning (2015)

    4) Rice University
    -An Introduction to Interactive Programming in Python (2014)

    5) Yale University
    -Financial Markets (2014)

    6) John Hopkins University
    -Regression Models (2014)
    -Statistical Inference (2014)
    -Getting and Cleaning Data (2014)
    -The Data Scientist's toolbox (2014)
    -R Programming (2014)

    7) Georgia Institute of Technology
    -Computational Investing (2013)

  • The Rise Of Automated Trading: Machines Trading the S&P 500 (Publication)
    More than 60 percent of trading activities with different assets rely on automated trading and machine learning instead of human traders. Today, specialized programs based on particular algorithms and learned patterns automatically buy and sell assets in various markets, with a goal to achieve a positive return in the long run. In this article, Toptal Freelance Data Scientist Andrea Nalon explains how to predict, using machine learning and Python, which trade should be made next on the S&P 500 to get a positive gain.


  • Languages

    Python, SQL, Visual Basic for Applications (VBA), R, Visual Basic, Java
  • Libraries/APIs

    NumPy, Pandas, SQLAlchemy, Matplotlib
  • Tools

    Microsoft Access, Microsoft Excel, Eclipse IDE, Git, Moodle, Microsoft Visual Studio, Subversion (SVN)
  • Storage

    MySQL, PL/SQL, Oracle PL/SQL, Microsoft SQL Server, JSON, SQLite
  • Other

    Data Analysis, Machine Learning, Mathematics, Algorithms, Data Engineering, Back-end, Algorithmic Trading, Key Performance Indicators (KPIs), Statistics, AWS, Trading, Algorithmic Trading Analysis
  • Paradigms

    Data Science
  • Platforms

    Oracle, MacOS, Windows, RStudio, Anaconda, Linux, Linux CentOS 7, DigitalOcean, Amazon Web Services (AWS)
  • Frameworks



  • Master's Degree in Computer Engineering
    1989 - 1998
    University of Padova - Padova, Italy


  • Machine Learning: Classification
    University of Washington via Coursera
  • An Introduction to Interactive Programming in Python
    Rice University via Coursera
  • Programming for Everybody: Python
    University of Michigan via Coursera
  • Practical Machine Learning
    Johns Hopkins University via Coursera
  • Machine Learning: Foundations
    University of Washington via Coursera
  • Machine Learning: Regression, Research Methodology and Quantitative Methods
    University of Washington via Coursera
  • High Performance Scientific Computing
    University of Washington via Coursera
  • Introduction to Computational Finance and Financial Econometrics
    University of Washington via Coursera
  • Financial Markets
    Yale University via Coursera
  • R Programming
    Johns Hopkins University via Coursera
  • The Data Scientist's Toolbox
    Johns Hopkins University via Coursera
  • Getting and Cleaning Data
    Johns Hopkins University via Coursera
  • Statistical Inference
    Johns Hopkins University via Coursera
  • Regression Models
    Johns Hopkins University via Coursera
  • Computational Investing
    Georgia Institute of Technology via Coursera
  • Mathematical Methods for Quantitative Finance
    University of Washington via Coursera

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