Senior ML Engineer (B2B Personalization)

Job description

**This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior ML Engineer (B2B Personalization) based in Brazil.** This is a high-impact opportunity to build a new intelligent personalization capability for B2B customers. You will lead the first technical cycle, from behavioral data exploration through a functional prototype and controlled validation. The role combines machine learning, data engineering, experimentation, generative AI, and product thinking. You will work with complex signals such as operational usage, CRM history, service interactions, customer journeys, and intent. Your goal will be to turn these signals into actionable intelligence that improves offers and customer experiences. The environment is highly collaborative, with close interaction across Product, UX, AI Engineering, Architecture, and business teams. This is an ideal role for a pragmatic ML professional who enjoys ambiguity, experimentation, and proving business value before scaling. ### 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.