Data Scientist

• Support the execution of tactical and operational initiatives defined in the product strategy;
• Monitor subscriber consumption behavior, engagement, and user experience in the product, contributing insights and opportunities to improve the subscriber journey;
• Monitor KPIs, analyze product performance, generate hypotheses and propose investigations that support improvement recommendations;
• Support data analysis and the creation of executive reports;
• Produce research briefings, oversee execution and consolidate findings;
• Support analyses and scenario building for discussions around content windows between products;
• Produce briefings for CRM actions and communication campaigns, and track their implementation;
• Consolidate results from acquisition, engagement and subscriber retention initiatives;
• Support integration routines with internal and external partner teams, including participation in cadences and agile ceremonies;
• Track request creation in Salesforce, monitor delivery statuses and consolidate inputs for the development of commercial plans.

Mid-level Product Analyst

• Develop Artificial Intelligence and Data Science solutions to support business decision-making;
• Build, train, validate, and optimize Machine Learning and Deep Learning models;
• Work on projects involving NLP, Generative AI, large language models (LLMs), and Retrieval-Augmented Generation (RAG) architectures;
• Develop data pipelines and integrations using Python and SQL;
• Design and implement statistical models applied to business problems;
• Develop dashboards, visualizations, and analytical applications using visualization libraries and AI frameworks;
• Contribute to the development and evolution of intelligent chatbots;
• Work with AWS cloud environments for development and deployment of solutions;
• Collaborate with cross-functional teams to define data-driven strategies;
• Ensure the quality, scalability, and performance of delivered solutions;
• Support technical presentations and communicate results to stakeholders.

Senior Artificial Intelligence Data Scientist

• You will be part of our Data Science team, responsible for developing, maintaining and evolving analytical solutions and machine learning models that support multiple business areas.
• We operate in a collaborative environment with a strong engineering culture, a focus on data quality, and continuous improvement.
• In this project, you will play a strategic role in migrating integrations, pipelines and data consumers currently running on AWS to the GCP ecosystem, ensuring operational continuity, reliability and adherence to data engineering and machine learning best practices.
• Perform the migration of connections, pipelines and data consumption processes from AWS to GCP for the company's Data Science projects.
• Adapt queries, integrations and data processing routines to run in the GCP environment, taking into account differences between services and SQL dialects.
• Execute functional tests, consistency validations and data sign-off after migration processes.
• Support the migration of analytical workloads and machine learning pipelines to equivalent services within the GCP ecosystem.
• Work closely with data engineering, architecture and MLOps teams to ensure stability and efficiency of migrated solutions.
• Identify and resolve issues related to performance, compatibility and governance during the cloud transition.
• Document technical processes, data flows and architectural decisions related to the migration.
• Contribute to standardization, automation and quality improvements in pipelines and analytical processes.

Senior Data Scientist – GCP Migration

• Desenvolver soluções técnicas para novas features e sustentar funcionalidades já existentes;
• Desenvolver e implementar modelos de Machine Learning e Inteligência Artificial;
• Atuar com modelagem, tratamento, transformação e governança de dados;
• Criar datasets e análises para responder perguntas estratégicas do negócio;
• Desenvolver dashboards e relatórios interativos no Power BI;
• Definir e acompanhar KPIs e métricas de negócio;
• Garantir performance, escalabilidade e qualidade das soluções e dados;
• Apoiar decisões estratégicas e operacionais por meio de análises orientadas a dados.

Senior NLP Data Scientist

• Perform funnel analysis
• Experimentation (A/B testing)
• Define metrics
• Predictive modeling
• User segmentation
• Deep dives

Senior Data Scientist

• Atuar de forma colaborativa em equipes multidisciplinares, mantendo comunicação clara e relacionamento harmonioso com diferentes áreas do negócio.
• Desenvolver, implementar e manter modelos matemáticos para simulação, otimização e suporte à tomada de decisão em processos da empresa.
• Traduzir problemas de negócio em formulações matemáticas (ex.: programação linear, inteira mista, não linear).
• Desenvolver soluções computacionais escaláveis, com foco em desempenho, robustez e manutenibilidade.
• Escrever código limpo, organizado e bem documentado, seguindo boas práticas de engenharia de software.
• Participar do ciclo completo de desenvolvimento das aplicações (análise, modelagem, implementação, testes, validação e evolução).
• Trabalhar em conjunto com usuários e stakeholders para entender necessidades, validar resultados e superar expectativas.

Cientista de Dados II

• Liderar a análise de dados e o desenvolvimento de modelos preditivos que irão potencializar nossos produtos e otimizar nossas operações.
• Criar, validar e implementar modelos de machine learning para aprimorar a análise de crédito.
• Realizar análises profundas para identificar tendências, padrões e oportunidades de negócio, comunicando os resultados de forma clara para as equipes de produto e liderança.
• Contribuir ativamente com ideias para aprimorar a coleta, o enriquecimento e a utilização dos dados na plataforma.

Cientista de Dados

• Collect, process, and analyze large volumes of structured and unstructured data;
• Develop statistical and machine learning models for forecasting and classification;
• Create dashboards and reports to track business metrics;
• Partner with business teams to identify data-driven opportunities;
• Implement and monitor models in production;
• Ensure data quality and governance;
• Explore new techniques and tools for data analysis and AI.

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