Top Data Science Jobs in Sao Paulo

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.

Manage and expand enterprise accounts in Latin America by prospecting, conducting discovery and product demos, executing complex sales strategies, hitting multi-million dollar targets, and collaborating with cross-functional teams to drive Snowflake adoption.

Build and deploy statistical and ML models (especially demand forecasting) to improve sales forecast accuracy. Analyze data, create actionable insights, collaborate with stakeholders and regional teams, set analytics standards, monitor model performance, and deliver dashboards and visualizations for supply chain decision-making.

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.

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.

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

The Fraud Strategy Specialist will design fraud prevention strategies, develop anomaly detection systems, and collaborate with multiple teams to enhance safety while enabling customer growth.

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

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.

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.

Hands-on credit data scientist responsible for building and monitoring predictive credit and churn models, creating feature books from transactional data, implementing AI-driven credit automation, backtesting portfolio strategies, building propensity-to-pay models for collections, and designing real-time KPI dashboards to track credit and recovery performance.

The role involves leading stakeholder relationships, developing AI/ML models, analyzing complex datasets, and generating insights to enhance client performance in the payments industry.

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.