Eriksson Monteiro, Developer in Aveiro, Portugal
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Eriksson Monteiro

Verified Expert  in Engineering

Deep Neural Networks Developer

Aveiro, Portugal
Toptal Member Since
June 4, 2018

Eriksson possesses a PhD in computer science and is an experienced full-stack developer and machine learning engineer. For the past five years, he's built web and mobile applications and has participated in several machine learning tasks in diverse data science fields. He's particularly good at working with application frameworks such as Django, Play Framework, and Node.js.


Blockchain, Ethereum, React, Django
BMD Software
MySQL, JavaScript, HTML, Hibernate, JPA, Scala, Java, Play Framework
TalkingData AdTracking Fraud Detection
Feature-driven Development (FDD), Gradient Boosting, MLlib, Apache Spark




Preferred Environment

Docker, Git, IntelliJ IDEA

The most amazing...

...thing I've built was a system that predicted whether a loan will default and the likelihood of loss incurred if it does.

Work Experience

Lead Developer

2018 - PRESENT
  • Built a system to process administrative documents over the Ethereum blockchain.
  • Worked on the front-end and built the UI using React.
  • Designed and developed the back-end using Django and the Django REST framework.
Technologies: Blockchain, Ethereum, React, Django

Product Owner

2017 - PRESENT
BMD Software
  • Managed the PACScenter product which is an all-in-one medical imaging platform for patient studies (storage, visualization, and sharing)—enabling simple and efficient workflows.
  • Developed core features for both the back-end and front-end.
  • Defined the strategy for new version releases.
Technologies: MySQL, JavaScript, HTML, Hibernate, JPA, Scala, Java, Play Framework

Kaggle Competitions Expert

2018 - 2018
TalkingData AdTracking Fraud Detection
  • Worked with a large dataset using big data tools like Apache Spark.
  • Built an algorithm that predicts whether a user will download an app after clicking a mobile app ad which helps to combat click fraud.
Technologies: Feature-driven Development (FDD), Gradient Boosting, MLlib, Apache Spark

Kaggle Competitions Expert

2018 - 2018
Toxic Comment Classification
  • Studied negative online behaviors e.g., toxic comments.
  • Built a multi-label model that’s capable of detecting different types of toxicity like threats, obscenity, insults, and identity-based hate.
  • Used a labeled dataset of comments from Wikipedia’s talk page edits.
Technologies: Support Vector Machines (SVM), Naive Bayes, Ensemble Methods, Gated Recurrent Unit (GRU), Embedded Development, GloVe, LSTM, Deep Learning

Kaggle Competitions Expert

2018 - 2018
Recruit Restaurant Visitor Forecasting
  • Predicted how many customers to expect in each day in a restaurant to effectively purchase ingredients and schedule staff members.
  • Developed a prediction model for this task; this was not easy to make because many unpredictable factors can affect restaurant attendance like the weather and local competition. It's even harder for newer restaurants with little historical data.
  • Worked with heterogeneous datasets.
Technologies: Gradient Boosting, Time Series, ARIMA, LSTM

Kaggle Competitions Expert

2017 - 2017
Sberbank Russian Housing Market
  • Created a prediction model capable of making predictions about realty prices so that renters, developers, and lenders are more confident when they sign a lease or purchase a building.
  • Developed algorithms which use a broad spectrum of features to predict realty prices, using a rich dataset that includes housing data and macroeconomic patterns.
Technologies: Validation, Feature-driven Development (FDD), Ridge Regression, Gradient Boosting

Kaggle Competitions Expert

2017 - 2017
Two Sigma Financial Modeling
  • Applied technology and systematic strategies to financial trading in order to forecast economic outcomes that can never be entirely predictable,.
  • Back-tested to validate regression models that predict financial time series.
Technologies: Ridge Regression, ExtraTreesRegressor, TensorFlow, Reinforcement Learning

Kaggle Competitions Expert

2016 - 2016
Santander Customer Satisfaction
  • Created a model that identifies dissatisfied customers.
  • Worked with hundreds of anonymized features to predict if a customer is satisfied or dissatisfied with their banking experience.
Technologies: Principal Component Analysis (PCA), Neural Networks, Ensemble Methods, Decision Trees, Random Forests

Kaggle Competitions Expert

2014 - 2014
Loan Default Prediction
  • Determined whether a loan will default, as well as the loss incurred if it does default.
  • Developed methods unlike traditional finance-based approaches to this problem, where one distinguishes between good or bad counter parties in a binary way, we sought to anticipate and incorporate both the default and the severity of the losses that result.
  • Built, as a team, a bridge between traditional banking, where we are looking at reducing the consumption of economic capital, to an asset-management perspective, where we minimized the risk to the financial investor.
Technologies: Gradient Boosting, Random Forests

Kaggle Competitions Expert

2013 - 2013
Job Salary Prediction
  • Built a prediction engine for the salary of any UK job advertisement so they can make huge improvements in the experience of users searching for jobs, and help employers and job seekers figure out the market worth of different positions.
  • Worked with a large dataset (hundreds of thousands of records) which was mostly unstructured text with few structured data fields. These were in a number of different formats because of the hundreds of different sources of records.
Technologies: Dimensionality Reduction, Tf-idf, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), GPT, Ridge Regression, Random Forest Regression

Dicom Anonymizer
A tool that uses machine learning to automatically anonymize medical images' pixel data. The main objective of this project was to provide a suitable alternative to a manual process in medical image anonymization. It uses a neural network to find sensitive information in the images and removes regions containing this information.

Scaleus is a semantic web data migration tool.

Dicoogle is an open-source PACS that's well fitted to small-medium sized institutions that want to install a PACS without a huge investment. It is based on a document-indexing model that can be shared over a LAN or a WAN, allowing the creation of federated views and extends the querying and retrieval over a large number of archives. It allows searching through free text queries as well as any DICOM tag. More information can be extracted from medical imaging repositories—offering increasing flexibility when compared to current query and retrieval DICOM services.


Java, SQL, Python, Scala, C#, JavaScript, HTML


Apache Spark, Play Framework, Django, JPA, Hibernate


Matplotlib, Pandas, NumPy, Scikit-learn, Keras, TensorFlow, React, LSTM, MLlib, Node.js, SciPy


Jupyter, Docker Compose, PredictionIO, Git, IntelliJ IDEA, Weka


Data Science, Scrum, Clean Code, DevOps, Concurrent Programming, Object-oriented Programming (OOP), Distributed Computing, Continuous Integration (CI), Parallel Computing, Agile Software Development, Unit Testing


Linux, Docker, MacOS, Windows, Ethereum, Blockchain


Deep Neural Networks, Data Mining, Machine Learning, Data Engineering, Predictive Analytics, RESTful Web Services, Regression Modeling, Classification Algorithms, Recommendation Systems, Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNN), Random Forests, Gradient Boosted Trees, Statistics, Big Data, Natural Language Processing (NLP), Text Mining, GitFlow, GPT, Generative Pre-trained Transformers (GPT), Gradient Boosting, ARIMA, Time Series, Reinforcement Learning, ExtraTreesRegressor, Ridge Regression, Random Forest Regression, Tf-idf, Dimensionality Reduction, Feature-driven Development (FDD), Validation, Deep Learning, Embedded Development, Gated Recurrent Unit (GRU), Ensemble Methods, Support Vector Machines (SVM), Decision Trees, Neural Networks, Principal Component Analysis (PCA), Naive Bayes, GloVe


Elasticsearch, MySQL, Memcached, Microsoft SQL Server, MongoDB, PostgreSQL

2012 - 2017

Doctor of Philosophy (PhD) Degree in Computer Science

MAP-i (University of Aveiro, Porto and Minho) - Portugal

2007 - 2012

Master's Degree in Computer Science

University of Aveiro - Portugal

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