The Data Science area is dedicated to the analysis and modeling of data, solving various and multidisciplinary problems with the aid of knowledge from different areas (mathematics, software engineering, and machine learning algorithms). Our focus is on implementing solutions with impact and generating value. With that in mind, there is a great focus on our development methodologies and problem-solving approach. Python is the main programming language, always applied in a distributed manner when necessary, allowing the implementation of scalable solutions.
Your role
As a Data Scientist, you will have the opportunity to solve real-world problems in some of the leading industries (Education, Science, Banking, Telecommunications, and Distribution). Your mission will be to apply various data science techniques such as training and modeling predictive models and analyzing and visualizing data to specific problems in these areas of society in order to obtain valuable information from the extraction, preparation, and manipulation of large amounts of data.
You will be responsible for:
- Implementing solutions for data extraction, transformation, and standardization that turn raw data into intelligible and relevant information;
- Researching, analyzing, and implementing modern and efficient algorithms to make the best use of the dataset under analysis;
- Performing exploratory data analysis and preparing reports based on the data to draw conclusions from them;
- Optimizing global solutions using machine learning models/information created by data analysis.
Stacks:
Python, PySpark, R
Job requirements
Academic background
Bachelor's or Master's degree in Computer Science, Information Systems, Mathematics, or related fields.
Professional experience
At least 1 year of experience as a Data Scientist, with strong knowledge of SQL, data manipulation, and visualization techniques.
Programming skills
Experience in Python programming, particularly with machine learning and mathematical libraries like NumPy, SciPy, scikit-learn, pandas, Keras, TensorFlow, PyTorch, and PySpark.
Machine Learning knowledge
Experience with supervised and unsupervised machine learning techniques, as well as data mining algorithms and pattern recognition.
Passion for data
Enthusiasm for data exploration, preparation, and conversion, turning raw data into actionable insights.
Languages
Fluency in English, spoken and written.
Nice to have:
- Experience in Microsoft Azure, GCP, and/or AWS;
- Knowledge of statistics and mathematical concepts such as linear and logistic regressions, data distribution, and normality tests.