meutudo.

Attorney

• Manage high-volume civil litigation (mass civil disputes).
• Handle administrative matters (official letters, Procon – consumer protection agency, and extrajudicial notices).
• Define and direct strategies and processes within the company’s caseload, with support and validation from senior team members.
• Manage queues with defined volumes and deadlines to meet internal SLAs when coordinating with partner teams and law firms’ operational requests (compliance/enforcement orders, payments, appeal analysis, inquiries, settlement analysis, etc.).
• Conduct baseline studies, create projects, and lead initiatives in partnership with the internal team and external law firms.

Legal Analyst – Mid-level

• Work on high-volume civil litigation
• Handle administrative matters (official letters, Procon proceedings and extrajudicial notices)
• Manage queues with defined deadlines and volumes to ensure compliance with internal timelines when coordinating with partner teams; handle operational queues requested by law firms (specific performance orders, payments, appeal analysis, inquiries, settlement analysis, and related matters).

Junior Legal Analyst

Product Designer

• Collaborate in building briefs, understanding problems, and defining solutions;
• Design user flows and journeys, ensuring clarity and a strong user experience;
• Support the definition and maintenance of the product communication strategy, in partnership with design, product, and content teams;
• Ensure visual and textual consistency across screens, flows, and interactions;
• Make decisions based on quantitative and qualitative data, using design methods and theories to inform choices;
• Participate in user research, testing, and validation;
• Share knowledge with the squad, contributing to the maturation of the design team.

Operations

• Knowledge management and taxonomy: evolve documentation and taxonomy structure; define conventions (tagging, naming, structure); ensure insights are organized and reusable.
• Knowledge repository: implement or evolve the repository, run tests with internal users, and integrate it with channels (e.g., Slack).
• Research Ops automation with AI: design and document automation solutions (e.g., parsing materials, summarization, data extraction/structuring, task generation), and maintain the workflow with high quality.
• Process efficiency: identify needs via internal workshops, redesign processes, document them, and train the team to increase self-service and reduce rework.
• Research operations (hands-on when needed): support or lead studies (e.g., desk research, light qualitative research, conversation analysis), and create/update service blueprints and journey artifacts in partnership with other researchers.
• Metrics and impact: monitor operational indicators (research usage, cycle time, adoption of standards) and communicate efficiency and quality improvements.

ResearchOps

Communications

• Assist in producing content and simple materials for internal communications (posts, notices, internal campaigns);
• Assist in administering and organizing satisfaction and climate surveys, helping with the collection and compilation of responses;
• Help organize and execute internal in-person and virtual events (holidays, engagement activities, corporate programs, etc.), supporting logistics, communications, and coordination;
• Assist in developing communication plans for internal programs, from save-the-dates to invitations and reminders;
• Keep the internal communications calendar up to date, tracking topics and deadlines with relevant teams;
• Support basic analysis of data and feedback from internal surveys to help evaluate initiatives and identify improvement opportunities;
• Support cost control, quotes, and budgeting for internal marketing initiatives;
• Contribute to vendor relationships, including requesting proposals, comparing quotes, monitoring contracts, and maintaining the supplier database.

Internal Communications Assistant

• Identify problems, risks and opportunities and communicate them in a structured manner to relevant stakeholders;
• Develop action plans, monitor their execution and ensure that responsible parties/stakeholders follow the directions;
• Serve as the focal point for the Operations team, providing support for technical questions, analyses, data interpretation and process automation;
• Collaborate with Product, Data and Technology teams to advance process automation and develop new solutions;
• Produce analyses, studies and strategic recommendations in partnership with management to support decision-making;
• Track, update and interpret the area’s key metrics, ensuring visibility and accuracy;
• Contribute to other activities related to the role, always focusing on efficiency and continuous improvement.

Senior Operations Analyst

Data Scientist

• Lead the team's day-to-day execution, ensuring technical quality, delivery cadence, and integration with Product, Engineering, and Credit/Risk areas.
• Coordinate the team (daily routines, priorities, rituals, technical review) and ensure consistent deliveries.
• Lead credit and risk modeling projects, unified scoring, and feature engineering/feature store initiatives.
• Ensure best practices in validation, versioning, and reproducibility (datasets, features, models, experiments).
• Define and track team metrics: model lead time, automated decision rate, drift/stability, and business impact.
• Collaborate with Engineering/Data teams to enable features, integrations, and deployment (batch and/or real-time).
• Support the design and analysis of tests/experiments and impact analyses (uplift, avoided loss, savings).
• Ensure documentation and governance (model cards, approval criteria, monitoring, limits, and risks).
• Mentor specialists/analysts, supporting technical growth and best practices.

Data Science and Machine Learning Manager

• Develop and evolve robust models that automate decisions and generate financial efficiency.
• Focus on advanced credit and risk modeling, unified scoring (retail and corporate customers) and feature engineering & feature store.
• Build and refine predictive models for credit/risk, propensity, delinquency, fraud, or operational efficiency (as prioritized).
• Design features and selection/transformation strategies, working closely with the data and engineering teams.
• Define and execute robust validations (offline/online), ensuring generalization and stability.
• Implement model monitoring (drift, stability, calibration, performance) and investigate degradations.
• Contribute to a unified score: metric consistency, comparability across segments and governance.
• Translate results into actionable decisions, estimating impact (uplift, losses avoided, savings).
• Document models (assumptions, limitations, risks, approval criteria) and support technical reviews.

Data Science and Machine Learning Specialist – AI Platform

Data Analyst

• Data Pipeline Ownership (Databricks): You will be the steward of the Legal team's data — responsible for ensuring data ingestion, processing, and high availability in Databricks.
• Automation and Efficiency (Python + No-code): You’ll streamline manual processes by developing Python scripts and building agile workflows with No-code tools (N8N, Make) to connect systems and automate routines, freeing the team to focus on high-value work.
• Data Visualization and Value Generation: Beyond creating dashboards in Superset, we’re looking for someone with business insight. Your goal is to turn complex data into clear visualizations and actionable insights that drive impact for the Legal team.