Top Data Science Jobs in Brazil

Support Mastercard Identity sales in Brazil by serving as the technical product expert for identity verification, fraud detection, and risk solutions. Lead discovery, solution design, proof-of-value testing, technical presentations, customer training, documentation, and onboarding. Explain machine learning, data science, APIs, and probability-based decisioning to technical and nontechnical stakeholders. Build customer relationships, attend conferences, provide market insights, and relay customer feedback to Product. Travel approximately 10%.

Lead the design, development, deployment, and continuous improvement of scalable production AI solutions. Build LLM, RAG, agentic AI, machine learning, and document intelligence systems using Python, Azure, DevOps, IaC, Docker, Kubernetes, and MLOps. Own architecture, testing, evaluation, monitoring, security, governance, and operational support while translating business needs into measurable outcomes. Provide technical leadership, mentorship, stakeholder collaboration, and influence enterprise AI engineering standards and platforms.

Own retention strategy across email, SMS, push notifications, and WhatsApp for two digital consumer brands. Lead lifecycle journeys, segmentation, experimentation, retention roadmaps, and cross-functional execution. Analyze revenue, conversion, repurchase, and LTV/CAC performance to drive growth and customer loyalty while reducing fatigue and unsubscribes.

Leads credit policy and pricing for current and new financial products. Designs experimentation agendas, applies AI to credit decisions, balances profitability with customer retention, and partners with Data, Product, Technology, Treasury, Strategy, and Growth. Initially operates as an individual contributor while building a senior credit team and developing scalable, data-driven risk structures for a marketplace ecosystem.

Leads and develops Applied Data Scientists across Brazil and India, delivering scalable data science, advanced analytics, and AI solutions for retail and CPG clients. Advises stakeholders, shapes commercial opportunities, manages end-to-end initiatives using agile methods, and promotes reusable science assets. Requires expertise in statistics, machine learning, customer and retail analytics, stakeholder management, team leadership, and Python, SQL, Spark, and Hadoop, plus fluent English and Portuguese.

Lead and scale advanced analytics and AI solutions across commercial, medical affairs, and clinical operations. Translate complex healthcare data into predictive models, next-best-action, and measurement frameworks; drive cross-functional partnerships, responsible AI governance, and team capability building to embed data-driven decision-making and measurable impact across the organization.

Leads Snowflake’s LATAM Solutions Engineering team in Brazil, managing sales engineers while supporting technical sales cycles, demonstrations, proofs of concept, customer presentations, and regional technical-win forecasting. Partners with sales, channel partners, product, engineering, marketing, and services teams to achieve quota goals, identify customer solutions, improve win rates, and expand into new territories. Responsibilities also include recruiting, interviewing, coaching, developing team talent, and advising revenue leadership on sales strategy and product needs.

Develop machine learning models and production analytics using large healthcare datasets for disease detection, patient outcome prediction, patient identification, and HCP targeting. Translate client requirements into scalable deliverables, validate new methodologies through client work, collaborate with engineering and product teams to productionize solutions, contribute to AI/ML proofs of concept, and mentor junior team members.

Design, develop, evaluate, and deploy scalable AI and machine learning solutions for fraud prevention, chargeback risk, and payment risk. Build production-grade APIs, data pipelines, and services; manage CI/CD, monitoring, testing, and troubleshooting for ML systems. Collaborate with data science, engineering, MLOps, and business teams to deliver impactful solutions, while mentoring colleagues and improving AI development practices.

Leads data science strategy across Mexico and Brazil, manages and mentors a distributed team, and remains hands-on building and deploying machine learning models. Establishes modeling standards, infrastructure, delivery practices, and technical roadmaps while partnering with credit leadership on underwriting, valuation, impact measurement, and P&L analysis. Requires strong applied machine learning expertise, prior data science leadership, business communication skills, and understanding of credit or underwriting.

Join the Data Marketing Consulting team to execute data-driven marketing projects: deliver analytics solutions, collaborate with Activation/Creativity/Strategy, build client relationships, communicate results, research technical approaches, and contribute best practices to ensure business value for clients.

The Solutions Architect leads technical strategies for large enterprise accounts, focusing on ML and AI adoption while mentoring team members.

As an Analytics Engineer, you'll refine data, build models, enable self-service analytics, and collaborate with various teams to improve customer insights.

The FP&A Analyst will drive projects in finance, support budgeting and cost reports, analyze financial performance, and collaborate with teams.

Lead statistical and data science rigor across Risk, Liquidity, Payments, and other squads. Review, validate, and hands-on improve statistical and machine learning models, applying causal inference, time series, Bayesian methods, optimization, and probability. Mentor data scientists, establish analytical quality standards, communicate uncertainty to stakeholders, and collaborate with Product, Risk, Engineering, and Compliance. Use AI-assisted workflows to accelerate delivery while maintaining methodological rigor.

Teach and support data science students via live lessons, Q&A, 1:1 calls, message support, project reviews, and actionable feedback. Troubleshoot code, explain concepts, and share industry best practices to help students complete the program.