• Help build and improve AI chatbots by training them with real conversation data • Clean and organize text data to make better machine learning models • Test how well our virtual agents are performing and find ways to improve them • Work alongside experienced consultants, developers, and conversation designers • Work with customers to understand requirements, pain points and clearly communicate progress and roadbloacks • Stay curious about new AI and NLP techniques

• Define the details of and execute the data science technical strategy, aligned to business objectives and overall data/analytics senior leadership vision and strategy. • Collaborate on the data science roadmap with data & analytics product management and stakeholders. • Mentor peer team members for effective delivery as well as to help build a high-performing data science and consultative business insights team. • Provide cross team technical reviews, thought partnership, and coaching. • Deliver effective and sustainable ML and GenAI products that drive measurable business value. • Lead cross domain programs that create reusable platforms, shared modeling components, and standardized evaluation frameworks. • Establish technical guardrails for modeling, feature engineering, experimentation, and MLOps across teams. • Design scalable ML/GenAI systems and reference designs, balancing performance, latency, cost, and compliance. • Review and approve models, ensuring rigor in causal inference, statistical methods, monitoring, and evaluation. • Introduce new methods and technologies when needed to unlock value or resolve domain‑level challenges. • Guide teams in designing robust experiments to measure outcomes and value. • Plan work and help with managing team capacity with data product owners/management. • Define MLOps strategy, including feature stores, observability, drift/performance monitoring, retraining policies, and rollback protocols. • Partner with engineering and platform teams to select tools, build roadmaps, and set SLAs for data quality, reliability, and model governance. • Ensure compliance with privacy, security, and Responsible AI requirements. • Establish ML platform governance for performance, cost optimization, and security compliance. • Partner with data platform team and solutions architects to solve data gaps or optimize data pipelines for advanced analytics. • Interacts, consults with and influences senior management at various levels of the organization, with various internal organizations (e.g. management, finance, marketing, academics, operations, legal). • Acts as liaison between the business operations and available data, working on various initiatives to maintain visibility and understanding of the data and decisions being made. • Promotes identification of best practices and fosters cross functional sharing of initiatives, innovation and best practices. • Performs other duties as assigned.

• Propose, develop and implement robust, scalable, and high-performance end-to-end Machine Learning models; • Implement recommendation and personalization models; • Develop hybrid intelligence systems combining Machine Learning and generative AI; • Provide technical leadership for projects and initiatives, ensuring the delivery of high-quality solutions; • Engage with business and product teams to define solutions with a systemic and comprehensive view; • Conduct complex analyses to extract valuable insights and drive positive business impact; • Develop and implement monitoring and measurement solutions for results; • Optimize the processing of large-scale data to ensure efficiency and accuracy; • Support other data scientists, fostering a continuous learning environment; • Collaborate with cross-functional teams to integrate data solutions across different areas of the company; • Stay up to date with the latest trends and technologies in data science and AI, applying them to continuously improve existing processes and models.

• Adequar modelos de inferência existentes à esteira produtiva corporativa • Parametrizar códigos conforme padrões técnicos e arquiteturais da plataforma • Garantir reprodutibilidade e versionamento dos modelos • Integrar modelos aos pipelines de dados produtivos • Validar consistência e performance dos modelos em ambiente operacional • Apoiar deploy e monitoramento de modelos em produção • Documentar fluxos de inferência e parametrizações realizadas • Atuar em conjunto com times de engenharia e arquitetura de dados