Leonardo Ignacio Córdoba, Developer in Buenos Aires, Argentina
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Leonardo Ignacio Córdoba

Verified Expert  in Engineering

Software Developer

Buenos Aires, Argentina
Toptal Member Since
March 27, 2019

Leonardo is starting a Ph.D. in CS after working as machine learning engineer in different areas such as transportation, banking, and marketing. He has been involved in the whole process of building models: business problem understanding, data manipulation, EDA, modeling, and deployment. He loves exchanging ideas and he is always looking for cutting edge algorithms and technologies to apply to new problems.


DBI - Havas Group
Amazon Web Services (AWS), Plotly, Scikit-learn, Docker, Linux, Google Cloud...
Digital House
Docker, Python
Prisma Medios de Pago
Linux, SQL, Teradata, R




Preferred Environment

Amazon Web Services (AWS), Slack, Jira, Visual Studio Code (VS Code), Git, Linux, Jupyter

The most amazing...

...machine learning model I've built helps banks clients by predicting if they will temporarily need a higher credit card limit, trained with 20 million cases.

Work Experience

Senior Data Scientist

2017 - PRESENT
DBI - Havas Group
  • Created a scalable serverless system to gather data from many APIs, using AWS stack. This system was made for a big network group in LATAM.
  • Built a machine learning model to asses the propensity of a visitor to a web page of buying a subscription. This model uses Google Analytics 360 data and was made for a big Argentinean newsgroup.
  • Built a clustering model to make automatic customer segmentation using demographic data and transactional data from a big Colombian company.
  • Designed machine learning proposals to tackle business problems in different companies in LATAM.
  • Completed exploratory data analysis of clients' databases, communicating insights and visualization of results.
Technologies: Amazon Web Services (AWS), Plotly, Scikit-learn, Docker, Linux, Google Cloud, Python

Data Science Professor

2017 - 2018
Digital House
  • Gave lectures on data science theory.
  • Taught Python, Pandas, Scikit-learn, GeoPandas, Imblearn, Seaborn, Plotly, etc.
  • Developed course materials.
Technologies: Docker, Python

Data Scientist

2017 - 2018
Prisma Medios de Pago
  • Used transactional data (credit and debit cards, ATMs, and POS) a machine learning model for predicting client default was made.
  • Created unsupervised machine learning models for clustering analysis were used to generate client segmentation.
  • Used geodata different geographic analysis where carried out. For example, market share per region.
  • Use transactional data (credit and debit cards, ATMs and POS) a machine learning model for predicting whether a client will exceed its credit limit (and have a transaction denial) was made. This model was used to temporarily raise the credit card limit of a client so as the bank could get more transactions properly done.In addition, the model used a bayesian hyperparameter tuning approach to auto-retrain after some time.
  • Deployed models using bash and Control-M. SQL User Store Procedures and bash programmes were used to deploy data manipulation stage in Teradata and in R, prediction stage using models and storing the results back to Teradata.
Technologies: Linux, SQL, Teradata, R

Data Scientist

2016 - 2017
Transportation Secretariat - Buenos Aires City Government
  • Processed public transportation ticketing system, with about 15 M transactions and 5 M GPS points each day.
  • Created automated detection of transfer patterns and of areas a high demand.
  • Developed automated bus stops finding using non-supervised machine learning techniques.
  • Designed data visualization using R and Carto (geographical data).
  • Developed an algorithm for building Origin-Destiny matrix.
  • Developed an algorithm for automatically detecting bus lines that don’t fulfill the number of daily services required each day.
  • Estimated public and private transport speeds and detecting of traffic problems.
Technologies: Linux, Microsoft SQL Server, PostGIS, PostgreSQL, R

Medical Appointment No Shows Analysis

A Kaggle dataset was used to develop a sample end-to-end project.
2019 - 2023

Ph.D. in Computer Science

University of Buenos Aires - Buenos Aires, Argentina

2016 - 2018

Specialist in Data Science

University of Buenos Aires - Buenos Aires, Argentina

2011 - 2015

Licenciate (Approximately Equivalent to a Bachelor + Master Degree) in Economics

University of Buenos Aires - Buenos Aires, Argentina


Certificate in Advanced English

University of Cambridge


Scikit-learn, Pandas


Plotly, GitHub, Jupyter, Git, Jira, Slack


SQL, Python 3, Python, R




Linux, Amazon Web Services (AWS), Docker, Visual Studio Code (VS Code)


PostgreSQL, Microsoft SQL Server, Google Cloud, PostGIS, Teradata, CartoDB



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