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.

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