Eriksson Monteiro
Verified Expert in Engineering
Deep Neural Networks (DNNs) 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.
Portfolio
Experience
- Python - 10 years
- Machine Learning - 5 years
- Data Science - 5 years
- Deep Neural Networks (DNNs) - 5 years
- Generative Pre-trained Transformers (GPT) - 5 years
- Data Engineering - 5 years
- Recommendation Systems - 5 years
- Natural Language Processing (NLP) - 5 years
Work Experience
Lead Developer
PinTecnologia
- 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.
Product Owner
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.
Kaggle Competitions Expert
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.
Kaggle Competitions Expert
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.
Kaggle Competitions Expert
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.
Kaggle Competitions Expert
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.
Kaggle Competitions Expert
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.
Kaggle Competitions Expert
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.
Kaggle Competitions Expert
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.
Kaggle Competitions Expert
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.
Project History
Dicom Anonymizer
https://hub.docker.com/r/bioinformaticsua/us-image-anonymizer/Scaleus
Dicoogle
http://dicoogle.comEducation
Doctor of Philosophy (PhD) Degree in Computer Science
MAP-i (University of Aveiro, Porto and Minho) - Portugal
Master's Degree in Computer Science
University of Aveiro - Portugal
Skills
Libraries/APIs
Matplotlib, Pandas, NumPy, Scikit-learn, Keras, TensorFlow, React, LSTM, MLlib, Node.js, SciPy
Tools
Jupyter, Docker Compose, PredictionIO, Git, IntelliJ IDEA, ARIMA, Weka
Languages
Java, SQL, Python, Scala, C#, JavaScript, HTML
Frameworks
Apache Spark, Play Framework, Django, JPA, Hibernate
Paradigms
Scrum, Clean Code, DevOps, Concurrent Programming, Object-oriented Programming (OOP), Distributed Computing, Continuous Integration (CI), Parallel Computing, Agile Software Development, Unit Testing
Platforms
Linux, Docker, MacOS, Windows, Ethereum, Blockchain
Storage
Elasticsearch, MySQL, Memcached, Microsoft SQL Server, MongoDB, PostgreSQL
Other
Deep Neural Networks (DNNs), Data Mining, Machine Learning, Data Science, Data Engineering, Predictive Analytics, RESTful Web Services, Regression Modeling, Classification Algorithms, Recommendation Systems, Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Random Forests, Gradient Boosted Trees, Statistics, Big Data, Natural Language Processing (NLP), Text Mining, GitFlow, Generative Pre-trained Transformers (GPT), Gradient Boosting, 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
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