Role Description We are looking for someone to work in a team that thinks big, works with attention to detail, collaborates daily, and shares responsibility. We believe in autonomy, the exchange of different experiences, and solutions built together. Here, growth means sharing knowledge, respecting different ways of thinking, and building trusting relationships while delivering quality technology. If you identify with this way of working, come join us. - Design, develop, test, and deploy advanced machine learning models and algorithmic solutions; - Lead data science initiatives from proof of concept (POC) to production; - Explore structured and unstructured data to identify opportunities and generate strategic insights; - Prepare and transform data for analytical and machine learning applications; - Monitor model performance and implement continuous improvements; - Build reusable and scalable code following best practices; - Collaborate with stakeholders to translate business problems into analytical solutions; - Work closely with Data Scientists, Analysts, and Data Engineers to deliver robust solutions; - Document models, processes, and outputs for technical and non-technical audiences; - Ensure timely delivery and ongoing maintenance of analytical solutions; - Act as a technical reference for projects and support knowledge sharing within the team. Qualifications - Advanced English; - Strong experience with machine learning techniques (regression, classification, clustering, etc.); - Experience delivering end-to-end data science projects (from exploration to production); - Proficiency in Python and machine learning libraries/frameworks; - Solid experience with data preparation and feature engineering; - Strong knowledge of SQL for data extraction and manipulation; - Experience with cloud environments, preferably Google Cloud Platform (GCP); - Familiarity with data modeling concepts; - Experience working in cross-functional teams and business-oriented problem solving. Requirements - Experience with optimization problems or graph-based solutions; - Previous experience in technical leadership or mentoring; - Spanish proficiency. Benefits - 🍽 Meal/Food Allowance - Caju multi-benefit card (credit format), offering flexibility for everyday needs. (CLT only) * - 🏥 Health Plan – Unimed - No waiting period or copayment for the holder. Possibility to include dependents (children, spouse, or stepchildren) with copayment. (CLT only) * - 🦷 Dental Plan – Uniodonto - Affordable options to include dependents. (CLT only) * - 💪 Wellhub - Access to gyms, physical activities, and wellness programs. - 🧘 Zenklub - For CLT: Two free sessions per month and special rates for additional sessions. For Cooperative/Contractor: Special rates for sessions. - 🛡 Life Insurance - More security for you and your family. (CLT only) * - 👶 Childcare Allowance - Financial support for Zallpers with children from 4 months to 6 years old, according to internal policy. (CLT only) * - 🎁 Baby Zallpy - A special gift to celebrate the arrival of new Zallpy babies. - 👥 Business Partner Support - Close and human-centered support from our People & Culture team throughout your journey at Zallpy. - 🌍 Volunteer Internal Communities - Diversity, Sports & Movement, and Technology. - 🎓 Educational Partnerships - Discounts on undergraduate and graduate programs, professional courses, and language schools. - ✈️ Experiences & Development - Participation in events, workshops, trips, and team-building activities. - 💼 Referral Program - Refer talents to Zallpy and receive bonuses from R$2,500 to R$5,000.

• Design, build, and own end-to-end machine learning models and AI-powered solutions, from problem framing through production deployment and impact measurement, focused on driving customer retention and operational excellence • Develop and enhance AI-driven tools that deliver explainable, actionable insights to Customer Success and Renewals teams, enabling data-informed decisions that protect and grow revenue • Partner closely with Customer Success, Renewals, Product, and Engineering to translate complex business challenges into well-scoped data science initiatives aligned to high-impact retention opportunities • Evaluate, prototype, and implement emerging AI/ML approaches, including generative AI and agentic workflows, to improve team efficiency and stakeholder value in a rapidly evolving landscape • Mentor and support fellow Data Scientists by setting high standards for analytical rigor, code quality, and documentation while contributing to best practices and team growth

• Build and maintain predictive models that drive marketing strategy, including player LTV, churn risk, CAC payback, and propensity-to-convert models • Own marketing attribution and incrementality analysis across paid channels (Meta, Google, TikTok, affiliates, influencers, etc.), helping the team understand what's actually driving growth • Quantify the causal impact and long-term business value of promotions, bonuses, and lifecycle campaigns, moving beyond surface-level engagement metrics to measure true incremental retention, monetization, and LTV impact • Apply causal inference techniques (geo experiments, synthetic control, diff-in-diff, CausalImpact, uplift modeling) to evaluate marketing investments where clean A/B tests aren't possible • Partner with growth marketers to design, run, and read out experiments: A/B tests, geo-tests, holdout studies, and creative tests • Develop and maintain dashboards and self-serve reporting that give marketing leaders real-time visibility into channel performance, cohort behavior, promo ROI, and funnel health • Clean, structure, and validate data across our marketing stack (ad platforms, MMP, internal event data, CRM) and partner with data engineering to improve our data models where needed • Translate complex analyses into clear, actionable recommendations for non-technical stakeholders • Continuously look for opportunities to automate, improve, and scale how the marketing team uses data

• Lead analytics and investigations that help the business understand exposure patterns and accumulation risk • Define and enforce data standards, quality controls, and best practices for exposure data across business lines • Own data quality and completeness - you not only collect feedback, but intuit what needs to be fixed from your industry experience, and collaborate with other departments on permanent solutions to reliably produce best in class exposure data • Lead the oversight into ingestion, transformation, and normalization of exposure data from internal and external sources • Validate and QA exposure data: identify anomalies, gaps, duplicates, inconsistencies, and drive improvements • Partner with actuarial, underwriting, catastrophe modeling, and product teams to understand their exposure needs • Develop and maintain exposure data documentation, data dictionaries, and process guidelines • Enable and support analytical use cases (e.g. accumulation risk, portfolio stress testing, scenario analysis) • Build, monitor and track data quality KPIs, build dashboards or alerts to surface issues proactively • Support ad hoc analysis to diagnose exposure trends and concentration risk • Provide guidance on integrating exposure data into downstream tools (e.g. modeling engines, pricing systems, BI)