Mid-Level Data Scientist

Job description

**This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Mid-Level Data Scientist based in Brazil.** As a Mid-Level Data Scientist, you will help transform digital and behavioral data into actionable insights that support product, marketing, and customer experience decisions. You will work across data integration, analytical modeling, experimentation, and machine learning in a collaborative environment. The role focuses on building reliable data pipelines, analytical layers, and well-governed datasets rather than large-scale streaming or traditional big-data workloads. You will explore user journeys, identify behavioral patterns and friction points, and develop models that enable personalization and better decision-making. Working closely with Product, Engineering, and Analytics teams, you will help strengthen data quality, measurement frameworks, and experimentation practices. This is an opportunity to contribute directly to data-driven initiatives while working with modern cloud, data platform, and development technologies. ### Accountabilities: - Translate digital event data from web and application environments into meaningful models of user behavior and customer journeys. - Conduct exploratory and advanced analyses to identify behavioral patterns, friction points, trends, and opportunities for improvement. - Generate actionable insights that inform product, marketing, customer experience, and personalization initiatives. - Define and structure analytical hypotheses, designing tests and experiments to validate findings and measure impact. - Design, develop, and scale Machine Learning and AI models focused on user behavior, personalization, and business outcomes. - Implement experimentation strategies including A/B testing, causal inference, and uplift modeling. - Collaborate with Product, Engineering, and Analytics squads to ensure accurate instrumentation, reliable integrations, and high-quality data. - Build and evolve frameworks and methodologies for measuring user behavior and the impact of business initiatives. - Support data democratization by making analytical findings clear, accessible, and actionable for stakeholders. - Contribute to stable batch-oriented ELT/ETL pipelines, analytical data models, and governed data layers. ### Requirements - Professional experience working as a Data Scientist, with hands-on experience applying data science techniques to real-world business problems. - Strong knowledge of digital behavioral analytics, including front-end events, tracking, user journeys, and interaction data. - Advanced proficiency in SQL, including query optimization, relational modeling, and analytical data modeling. - Practical experience with Snowflake, including data warehousing, roles, tasks, performance optimization, and cost management. - Hands-on experience with dbt, including models, tests, sources, and exposures. - Experience building and maintaining data pipelines with Airflow, including DAGs, sensors, retries, and SLAs, as well as AWS Lambda. - Knowledge of PostgreSQL, including ingestion, basic replication or CDC, maintenance, and database routines. - Strong Python experience for data applications, particularly with Pandas, plus experience developing APIs with FastAPI. - Practical knowledge of batch-oriented ELT/ETL, Git-based version control, and CI/CD practices for safely deploying data pipelines and dbt models. - Understanding of data security and governance principles, including access control, data lineage, documentation, and sensitive data management. - Familiarity with cloud and data platform technologies such as AWS, Snowflake, and Airflow. - Experience with CI/CD tools such as GitHub Actions, GitLab CI, or AWS CodeBuild is a plus. - Knowledge of Infrastructure as Code using CloudFormation or Terraform is an advantage. - Familiarity with observability tools such as CloudWatch, Grafana, or Prometheus is a plus. - Strong analytical thinking, attention to detail, problem-solving skills, and the ability to communicate technical insights clearly. - Ability to collaborate effectively with multidisciplinary teams and translate complex data findings into understandable business recommendations. ### Benefits - Health and dental insurance. - Meal and food allowance. - Childcare assistance. - Extended parental leave. - Access to fitness, health, and wellness partnerships through Wellhub and TotalPass. - Profit-sharing program (PLR). - Life insurance. - Continuous learning opportunities through an internal learning platform. - Access to online courses and professional development resources. - Language learning platform. - Discounts through partner programs. - Online resources focused on physical health, mental health, and overall well-being. - Pregnancy and responsible parenthood courses. - Opportunities to develop expertise across Data Science, Machine Learning, cloud, data engineering, and modern data platforms. - Collaborative environment with Product, Engineering, and Analytics teams. - For candidates residing in the Campinas Metropolitan Region, office attendance is required according to the applicable workplace policy.