Senior ML Engineer (B2B Personalization)

Accountabilities

• Lead the technical development of the first B2B personalization capability, from data exploration and hypothesis definition to prototyping and validation in a controlled environment.
• Explore large behavioral and operational datasets to identify meaningful patterns, similarities, and changes across enterprise accounts.
• Develop behavioral representations and features that combine operational, commercial, service, and contextual signals.
• Experiment with clustering, embeddings, similarity models, propensity modeling, recommendation systems, and next-best-action approaches where appropriate.
• Evaluate when different machine learning techniques create genuine business value rather than adding unnecessary complexity.
• Explore generative and agentic AI applications that can translate recommendations into concrete actions, such as personalized commercial proposals.
• Build functional prototypes and MVPs rapidly within sandbox or controlled environments, creating a clear path toward scalable capabilities.
• Design experimentation and measurement frameworks using appropriate controls, business metrics, statistical analysis, uplift, and causal approaches.
• Establish feedback loops that enable models and personalization strategies to continuously improve.
• Collaborate closely with Product, UX, AI Architecture, Engineering, and business specialists to translate analytical insights into commercial decisions and customer experiences.
• Help define the technical evolution from prototype to a scalable, production-ready capability.

Requirements

• Proven experience applying Machine Learning to real-world business problems, with a strong understanding of experimentation and model evaluation.
• Hands-on experience with unsupervised learning, clustering, feature engineering, recommendation systems, propensity modeling, similarity models, and behavioral embeddings.
• Familiarity with techniques such as K-Means, hierarchical clustering, DBSCAN/HDBSCAN, Gaussian Mixture Models, PCA/UMAP, vector embeddings, nearest-neighbor methods, and similarity search; practical judgment in selecting the right approach is more important than knowing every technique.
• Strong Python and advanced SQL skills, with the ability to work with large datasets, structured data, and behavioral or operational event data.
• Experience with data pipelines, feature pipelines, data lakes or warehouses, and APIs or services for making models available.
• Ability to move beyond exploratory analysis and deliver working prototypes without needing to be a specialized Data Engineering infrastructure expert.
• Strong understanding of A/B testing, experiment design, causal inference, uplift modeling, statistical analysis, business metrics, model evaluation, and feedback loops.
• Experience with Generative AI and LLMs for contextualized recommendations or content generation is a strong differentiator.
• Familiarity with agentic architectures, A2A concepts, or open orchestration protocols such as MCP is desirable.
• Knowledge of Customer Data Platforms, personalization engines, real-time decisioning, marketing technology, digital products, experimentation platforms, feature stores, vector databases, MLOps, or real-time ML is a plus.
• Experience with B2B environments, mobility, fleet management, or similarly complex account-based businesses is desirable but not mandatory.
• Strong intellectual curiosity, product mindset, and comfort working with ambiguous or loosely defined problems.
• Ability to transform hypotheses into practical prototypes quickly and distinguish technical complexity from genuine customer value.
• Strong communication skills, with the ability to explain sophisticated technical concepts clearly to business stakeholders.
• Ability to collaborate effectively across Data Science, Engineering, Product, UX, Architecture, and business functions.

Benefits

• Health and dental insurance.
• Meal and food allowance.
• Childcare assistance.
• Extended parental leave.
• Access to fitness, health, and wellness professionals through Wellhub/Gympass and TotalPass.
• Profit-sharing program (PLR).
• Life insurance.
• Continuous learning through an internal learning platform.
• Discounts club and partner benefits.
• Online platform focused on physical health, mental health, and overall well-being.
• Pregnancy and responsible parenting courses.
• Partnerships with online learning platforms.
• Language-learning platform.
• Remote work opportunity in Brazil.
• A collaborative environment focused on innovation, continuous learning, and high-impact technology projects.
• Opportunities to work across Data & AI, product, engineering, and business domains.