Top Data Science Jobs in Rio de Janeiro

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.

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.

Leads Rare Brazil’s monitoring, evaluation, learning, and program data function. Manages staff and consultants, oversees budgets and workplans, ensures data quality, and delivers monitoring and donor reporting outputs. The role develops M&E frameworks, manages ecological and socioeconomic data collection, oversees data governance and dashboards, coordinates university and government partnerships, and supports adaptive management for coastal fisheries conservation programs.

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

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.