Wellington Rodrigo Monteiro, Developer in Curitiba - State of Paraná, Brazil
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Wellington Rodrigo Monteiro

Verified Expert  in Engineering

Machine Learning Engineer and Developer

Location
Curitiba - State of Paraná, Brazil
Toptal Member Since
October 5, 2022

Wellington is a senior data scientist and machine learning engineer with 10+ years of experience designing AI systems, back-end projects, and integrations with existing solutions respecting enterprise architecture requirements for large companies such as ExxonMobil and BRF. He specializes in complex machine learning models such as ensemble models, explainable artificial intelligence, and fair AI. Wellington also works with multi-objective optimization problems in critical applications.

Machine LearningEnsemble MethodsBig DataSQLGitPythonPandasScikit-learnAzure FunctionsData VisualizationRecommendation SystemsAzure Machine LearningPyTorchAutoMLIT Project ManagementExplainable AIMLOps

Portfolio

Nubank
Python, Scala, Databricks, Git, Agile, Data Visualization...
Pontifícia Universidade Católica do Paraná
Technical Writing, Python, SQL, Machine Learning, DevOps, Agile...
BRF
Machine Learning Operations (MLOps), Agile Data Science...

Experience

SQL - 10 yearsPython - 7 yearsMachine Learning - 7 yearsData Science - 7 yearsPandas - 6 yearsSupervised Learning - 6 yearsEnsemble Methods - 5 yearsExplainable Artificial Intelligence (XAI) - 3 years

Availability

Part-time

Preferred Environment

Cloud, Python, Pandas, Ensemble Methods, Explainable Artificial Intelligence (XAI)

The most amazing...

...thing I've implemented is novel algorithms directly from scientific conferences to real-world, complex scenarios while working between academia and industry.

Work Experience

Lead Machine Learning Engineer

2022 - PRESENT
Nubank
  • Migrated complex features in big data structures to optimize space and cost.
  • Incorporated explainable artificial intelligence (XAI) and fairness capabilities into complex ML classifiers handling big data.
  • Trained other data scientists and machine learning engineers on MLOps, XAI, and fairness processes.
Technologies: Python, Scala, Databricks, Git, Agile, Data Visualization, Amazon Web Services (AWS), Data Analysis, Scikit-learn, Machine Learning, Big Data, XGBoost, Optimization, Multi-objective Optimization

Professor

2018 - PRESENT
Pontifícia Universidade Católica do Paraná
  • Served as an honorary professor of the Polytechnic School for two years, in 2021 and 2022.
  • Taught technical courses such as machine learning, programming, and databases.
  • Held non-technical and mixed technical courses on project management, DevOps, and enterprise architecture.
Technologies: Technical Writing, Python, SQL, Machine Learning, DevOps, Agile, Amazon Web Services (AWS), Data Visualization, Figma, Scikit-learn, XGBoost, Optimization, Multi-objective Optimization

Data Science Chapter Lead

2020 - 2022
BRF
  • Oversaw a $3 million AI project spanning nine months, consisting of multiple time-series forecast methods.
  • Implemented a time-series model to forecast vaccination rates within the company offices and factories with an accuracy of over 90%.
  • Defined the data science technical architecture used across all company business units.
Technologies: Machine Learning Operations (MLOps), Agile Data Science, Explainable Artificial Intelligence (XAI), Python, Unsupervised Learning, Supervised Learning, Azure Functions, Azure Machine Learning, LightGBM, Ensemble Methods, SQL, Data Visualization, REST APIs, Data Analysis, Scikit-learn, Recommendation Systems, Machine Learning, Big Data, PyTorch, XGBoost, Optimization, Multi-objective Optimization

Data Science Tech Lead

2019 - 2020
BRF
  • Developed an ML regressor to predict retail sales of over 500 SKUs and 100 sites daily.
  • Built a recommender system to provide B2B recommendations for grocery stores in over 4,000 cities.
  • Developed an ML regressor to predict road accident risks.
Technologies: Python, Spark, Databricks, Azure Functions, Serverless Architecture, Docker, Supervised Learning, Explainable Artificial Intelligence (XAI), Unsupervised Learning, Azure Machine Learning, Pandas, LightGBM, Ensemble Methods, Scikit-learn, AutoML, Machine Learning Operations (MLOps), Recommendation Systems, Data Visualization, REST APIs, Data Analysis, Machine Learning, Big Data, XGBoost, Optimization, Multi-objective Optimization

Senior Data Scientist

2018 - 2019
BRF
  • Created 70 web scraping applications collecting public data such as industrial throughput, exchange rates, market indicators, and sector KPIs.
  • Developed a classifier for animal health risk detection with over 90% accuracy.
  • Created a regressor to predict poultry weight with an error range of 50 g.
Technologies: Python, Supervised Learning, Web Scraping, Data Warehousing, Azure Data Lake, Azure Machine Learning, Azure Data Factory, Scikit-learn, Machine Learning, Big Data

International Projects Analyst

2015 - 2018
BRF
  • Contributed to full-cycle implementation of an ERP for operations in China, one of the company's largest customer markets.
  • Participated actively in four full-cycle projects, leading integrations with legacy software in M&A projects in Argentina, Europe, the Middle East, and Thailand.
  • Created software to manage all weighbridges simultaneously in the company, with an estimated economy of $10,000 per month.
Technologies: APIs, Python, Machine Learning Operations (MLOps), DevOps, Agile DevOps, Scrum, REST APIs

SAP MM PTP Analyst

2013 - 2015
ExxonMobil
  • Implemented and developed non-SAP applications in .NET and C# used in all European retail sites.
  • Developed and maintained all procure-to-pay (PTP) processes related to acquisition and supply chain in the Americas, Africa, Europe, and Asia-Pacific in the upstream and downstream parts and chemical businesses.
  • Optimized and implemented legacy databases used by critical company applications.
Technologies: IT Project Management, C#, .NET, APIs, SAP Materials Management (MM), SQL

Recommender System for B2B

https://customers.microsoft.com/en-us/story/1338924265808999371-brf-consumer-goods-azure-machine-learning
An AI algorithm that recommends new products for small businesses and owners of small stores such as bakeries, restaurants, and cafés. I was in charge of developing the algorithm, evaluating it, and explaining complex technical concepts to the stakeholders.

Advanced Analytics Models in Agriculture

https://fiesc.com.br/pt-br/imprensa/brf-investe-r-10-milhoes-na-jornada-commodities-40
A project that involved 50+ advanced analytics models using machine learning, statistics, and econometrics to predict market behavior and agriculture production worldwide. I was the technical lead and spearheaded the center of excellence in charge of this project.
2019 - 2022

Doctorate Degree in Industrial and Systems Engineering

Pontifical Catholic University of Paraná - Curitiba, Brazil

2016 - 2018

Master's Degree in Industrial and Systems Engineering

Pontifical Catholic University of Paraná - Curitiba, Brazil

2009 - 2013

Bachelor's Degree in Computer Engineering

Pontifical Catholic University of Paraná - Curitiba, Brazil

Libraries/APIs

Pandas, Scikit-learn, XGBoost, PyTorch, TensorFlow, REST APIs

Tools

Git, AutoML, Azure Machine Learning, SAP Materials Management (MM), Figma

Languages

SQL, Python, Scala, C#

Paradigms

Data Science, Serverless Architecture, DevOps, Scrum, Agile

Platforms

Azure Functions, Databricks, Docker, Amazon Web Services (AWS)

Frameworks

.NET, Spark, LightGBM

Storage

Databases

Other

Multi-objective Programming, Genetic Algorithms, Explainable Artificial Intelligence (XAI), Supervised Learning, Ensemble Methods, Machine Learning, Big Data, Multi-objective Optimization, Optimization, IT Project Management, APIs, Machine Learning Operations (MLOps), Recommendation Systems, Data Visualization, IT Systems Architecture, Agile DevOps, Web Scraping, Data Warehousing, Azure Data Lake, Azure Data Factory, Unsupervised Learning, Agile Data Science, Technical Writing, Cloud, Data Analysis

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