Mundiale

Analyst

• Diagnose learning needs with business areas and propose customized solutions;
• Develop and evolve learning paths and programs for the Controllership and Commercial SaaS teams;
• Create creative and engaging learning experiences using active methodologies and AI resources;
• Manage L&D metrics, analyze results and propose continuous improvements;
• Work on development projects for soft, hard and business skills across different organizational levels;
• Support climate and engagement initiatives, connecting insights to learning and organizational culture.

Junior Training and Development Analyst

Data Analyst

• Monitor sprints and perform validations of product changes;
• Run database queries for product analysis;
• Build Power BI dashboards/views to facilitate operational monitoring;
• Conduct historical analyses of operations.

Customer Success Manager

• Manage the ongoing process to prevent churn, promote customer satisfaction, support customers in achieving expected results, and foster loyalty.
• Develop and implement strategies to maximize customers' growth potential.
• Conduct regular performance analyses of customers, identifying opportunities and potential risks to prevent churn.
• Identify and strengthen relationships with customers to ensure satisfaction and promote upsell/cross-sell.
• Create commercial strategies for each customer, transforming the partnership into a long-term relationship.
• Collaborate with internal teams such as Product and Marketing to ensure delivery of effective and satisfactory solutions to customers.
• Monitor overall portfolio KPIs and dig into results for the most critical customers to ensure delivery quality.

Customer Success Assistant

• Strengthen customer relationships throughout the customer journey, fostering connection, satisfaction, and a positive, memorable experience.
• Develop and execute engagement and retention initiatives aimed at reducing churn and building long-term partnerships.
• Collect and analyze product suggestions, bug reports, and feedback, serving as a bridge between customers and internal teams to drive continuous improvement of the solutions.
• Monitor the evolution of solutions and design strategies to improve the customer journey, ensuring the experience aligns with customer needs and expectations.
• Stay up to date on solution features and updates, providing consultative, effective, and informative support.
• Validate customers' use of solution features and identify adoption gaps, ensuring customers realize the platform's full potential and increasing account profitability.
• Monitor key customer portfolio indicators, dive into critical cases to ensure quality deliveries, and implement corrective actions when necessary.
• Collaborate with internal areas such as Product, Marketing, Customer Success, and Technology to deliver integrated solutions and a seamless customer experience.
• Identify expansion opportunities within the active customer base, driving consultative upsell and cross-sell initiatives focused on customer value.

Customer Success, Enterprise

Product Manager

• Manage and evolve conversational products, ensuring alignment with the company's strategy;
• Support the discovery process for new features in partnership with the Customer Success (CS) team and internal stakeholders;
• Translate business requirements and opportunities into clear user stories and requirements for the technical team;
• Propose improvements, new flows, and features based on results, benchmarks, and identified needs;
• Prioritize deliverables, balancing impact, effort, and technical feasibility;
• Monitor product performance and suggest data- and feedback-driven adjustments;
• Collaborate with Customer Success and Sales teams to ensure consistent, user-centered deliveries.

Associate Product Manager

Data Scientist

• Translate technical results into clear, actionable business insights.
• Design and run systematic experiments and evaluate model performance using appropriate metrics.
• Apply data augmentation and balancing techniques to optimize model generalization.
• Create visualizations and statistical analyses (distributions, co-occurrences, n-grams, embeddings).
• Collaborate with Machine Learning engineers and product teams to prototype analytical solutions and predictive models.

Machine Learning Engineer

• Design, train, and validate language models (LLMs) applied to NLP.
• Build and maintain scalable pipelines for text pre-processing and post-processing.
• Develop and integrate services and APIs for model consumption within messaging systems and conversational automation.