Senior Data Scientist

Insight IT

. Develop, train, and validate Machine Learning models applied to real-world business problems .

Posted 9/22/2026full-timeSão Paulo • BrazilSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing, training, and deploying Machine Learning models using Databricks and Python, with a strong focus on feature engineering, data preparation, and model performance monitoring in AWS environments.

Highest-signal resume keywords

Machine Learning Model DevelopmentDatabricks Ecosystem ExperiencePython Proficiency for Data ScienceFeature Engineering and Data PreparationModel Deployment and Monitoring

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningFeature EngineeringData PreparationModel DeploymentModel MonitoringSupervised LearningUnsupervised LearningAutoMLScikit-LearnXGBoost

Tools & Technologies

DatabricksAWSMLflowLightGBM

Industry Keywords

Data LakeBig DataAnalytical ModelsData Science Best PracticesRecommendation Modeling

Tech Stack

Tools & technologies

AWSPythonScikit-Learn

About the role

Key responsibilities & impact

Develop, train, and validate Machine Learning models applied to real-world business problems
Work with large volumes of data stored in the corporate Data Lake on AWS
Use the Databricks ecosystem for model experimentation, training, and operationalization
Build data pipelines and model training pipelines
Use Databricks AutoML capabilities to accelerate experimentation and model generation
Perform feature engineering and data preparation for analytical models
Implement and maintain deployment and scoring routines for machine learning models in production
Monitor model performance, assessing degradation and the need for retraining
Collaborate with data engineering, BI, and data product teams to transform data into scalable analytical solutions
Support the advancement of the company’s intelligence platform through data science and machine learning best practices

Requirements

What you’ll need

Experience with Databricks
Experience developing supervised and unsupervised machine learning models
Knowledge of AutoML in Databricks
Experience deploying machine learning models to production
Knowledge of model scoring and inference pipelines
Experience with feature engineering and data preparation for analytical models
Strong proficiency in Python applied to data science
Experience with scikit-learn, XGBoost, and LightGBM libraries
Experience working with data in Data Lake and Big Data environments
Preferred: Experience with MLflow
Preferred: Experience with machine learning model monitoring and governance
Preferred: Experience working on recommendation, segmentation, or propensity modeling projects
Preferred: Experience in AWS data environments
Preferred: Experience in advanced analytics and data product projects