Develop, train, and validate supervised and unsupervised Machine Learning models;
Perform statistical and mathematical analyses to identify patterns, trends, and opportunities;
Work with exploration, preprocessing, and modeling of large volumes of data;
Build analytical pipelines and data preparation processes;
Create predictive studies, segmentations, and recommendation models;
Support business areas in data-driven decision making;
Develop dashboards and executive presentations with analytical insights;
Monitor performance and accuracy of deployed models;
Collaborate with Data Engineering, BI, and business teams;
Ensure best practices for documentation, governance, and model versioning.

Experience in Data Science and Machine Learning;
Strong knowledge of Statistics;
Probability;
Applied Mathematics;
Predictive modeling;
Experience with languages: Python; SQL;
Experience with Machine Learning libraries and frameworks: Scikit-Learn; Pandas; NumPy; TensorFlow or PyTorch;
Knowledge of: Feature Engineering; Model validation; Performance metrics; Clustering; Regression; Classification;
Experience manipulating and analyzing large volumes of data;
Knowledge of Cloud environments: AWS; Azure or GCP;
Experience with visualization tools: Power BI; Tableau; Matplotlib;
Knowledge of version control with Git;
Experience with agile methodologies.

15 days paid leave;
Clude Saúde (online consultation platform);
Birthday day off + gift;
AWS partnership;
Language assistance;
TotalPass;
Support for specialization/certification (Postgraduate/MBA and AWS Certification);
Referral bonus;
Merit platform.

Data Scientist

Key skills