Brenda Oliveira Ramires, Developer in São Paulo - State of São Paulo, Brazil
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Brenda Oliveira Ramires

Verified Expert  in Engineering

Data Scientist and Machine Learning Developer

São Paulo - State of São Paulo, Brazil
Toptal Member Since
October 30, 2020

Brenda is a data scientist trained in computer engineering, and she's passionate about optimizing processes in retail and consumer goods. She has deep expertise in using machine learning and data science to research and implement optimized strategies for retail assortment and pricing. Brenda excels at developing and delivering elegant data and machine learning solutions while working as a remote freelance developer.


Scikit-learn, Spark, Linear Regression, Clustering, Hadoop, Spark SQL, Python...
Big Data Brasil
Amazon Web Services (AWS), Gradient Boosting, Decision Trees...
Watermelon Tecnologia
Agile Software Development, SQL, Swift, iOS, Android API, Java, MySQL...




Preferred Environment

Amazon Web Services (AWS), Spark, Python, PyCharm, Jupyter Notebook

The most amazing...

...project I've done was to co-develop and maintain a data-driven CRM that analyzes customer behavior and performs basket analyses.

Work Experience

Data Scientist

2020 - PRESENT
  • Developed a model that helped forecast the demand for a product in a certain time period.
  • Performed custom analyses to help business understand their customers and make smarter decisions.
  • Used machine learning algorithms, such as clustering, to analyze retail transactional data and understand customer behavior.
Technologies: Scikit-learn, Spark, Linear Regression, Clustering, Hadoop, Spark SQL, Python, SQL, MySQL, Data Cleaning, Large Data Sets, Data Analytics, Data Scientist, Analytics

Data Scientist

2018 - 2019
Big Data Brasil
  • Implemented demand forecasting models to identify expansion opportunities for large consumer goods companies.
  • Developed data-driven CRM strategies based on analysis of customer behavior and basket analyses.
  • Used clustering and regression models to improve product assortment strategies.
  • Developed web crawlers and ETL pipelines to collect and process customer data.
  • Used visualization tools to develop reports and dashboards to track and display KPIs and other important metrics.
Technologies: Amazon Web Services (AWS), Gradient Boosting, Decision Trees, Regression Modeling, Pandas, Scikit-learn, SQL, Python 3, Python 2, MySQL, Data Cleaning, Large Data Sets, Unstructured Data Analysis, Data Gathering, Data Analytics, Data Scientist, Analytics

Software Developer

2015 - 2018
Watermelon Tecnologia
  • Developed numerous applications with Java and SQL Server.
  • Built mobile applications for the Android operating system.
  • Developed multiple mobile applications for iOs devices.
Technologies: Agile Software Development, SQL, Swift, iOS, Android API, Java, MySQL, Unstructured Data Analysis, Analytics

Demand Forecasting

Demand forecasting models that helped a company expand its presence in Brazil by opening additional stores. We used aggregate information about competitors that the client purchased from a consulting firm, the client's own data, and the data the client collected about Brazil to model the demand per region, and how much of that demand was satisfied by the competitors. The result was a map highlighting areas with great potential for new stores.

Data-driven CRM

A data-driven CRM solution for a retailer in Brazil. The company wanted to use the data from previous purchases to identify types of customers and create more personalized discounts. The solution used basket analysis to identify products that were often bought together and analysis of past customer behavior to identify which types of discounts had worked better and in which phase of the relationship with the brand the customer was; for example, recently started the relationship, coming back after some time of not shopping, or highly loyal.

In the end, the solution identified the best products to apply a discount to and the clients that needed to receive the discount in order to achieve a goal from the business side; for example, make the client loyal to the brand, retain a casual buyer, or increase average ticket.

Automatic Data Collection

A pipeline that collected, cleaned, and organized data to be used in different projects by all the other data scientists on the team. Using crawlers and public databases, we collected data to be used in our projects. Each team identified databases from which to extract data and processed the datasets in their own way. As a result, teams often processed databases that had already been processed by other teams.

To save time for our data scientists, I established a team to centralize data collection and processing. We automated the execution of crawlers after creating a standard for how a crawler should function and the output it should generate. We saved this first output and the form we created with important features that we made available to everyone. We kept the first output because we could always create more features from the original dataset. In the end, we all had one place to go to look for data, and we didn't have to waste time processing the same data again.
2019 - 2020

Master's Degree in Informatics and Applied Mathematics

University of São Paulo - São Paulo, Brazil

2013 - 2017

Bachelor's Degree in Computer Engineering

University of Campinas - Campinas, São Paulo, Brazil


Pandas, Scikit-learn, Android API, Luigi


Spark SQL, PyCharm, Seaborn


Data Science, Agile Software Development


Hadoop, Spark, Scrapy


Python, SQL, Python 3, Python 2, Java, Swift, C




iOS, Jupyter Notebook, Amazon Web Services (AWS)


Clustering, Regression Modeling, Decision Trees, Machine Learning, Data Cleaning, Large Data Sets, Unstructured Data Analysis, Data Analytics, Data Scientist, Linear Regression, Gradient Boosting, Random Forests, Optimization, Statistics, Dashboards, Artificial Intelligence (AI), Data Gathering, Analytics

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