<div><span>Who we are:</span></div><div><br /></div><div><span>CloudWalk is a fintech company reimagining the future of financial services. We are building intelligent infrastructure powered by AI, blockchain, and thoughtful design. Our products serve millions of entrepreneurs across Brazil and the US every day, helping them grow with tools that are fast, fair, and built for how business actually works. Learn more at </span><a href="http://cloudwalk.io">cloudwalk.io</a><span>.</span></div><div><br /></div><div><span>About the Role:</span></div><div><br /></div><div><span>We’re hiring a Data Scientist with strong foundations in </span><b><span>statistics, causal inference, and experimental design.</span></b><span> Your work will be centered on rigorous analysis: designing robust experiments, validating assumptions, and extracting clear insights from complex data. This is not a “dashboards and KPIs” role — it’s about applying statistical thinking and careful modeling to answer hard questions and guide product direction.</span></div><div><br /></div><div><span>We want someone who can think critically with data, separate signal from noise, and provide results that stand up to scrutiny.</span></div><h3>What Youll Do: </h3><ul><li>Design, run, and analyze A/B tests and other experimental frameworks.</li><li>Apply statistical inference, hypothesis testing, and causal analysis to evaluate product changes.</li><li>Build, tune, and interpret classical ML models (regression, classification, ranking) with a focus on explainability and robustness.</li><li>Use rigorous exploratory analysis to uncover hidden patterns or validate product hypotheses.</li><li>Develop reproducible workflows for experimentation and modeling.</li><li>Collaborate with product and engineering to ensure experiments and analyses translate into well-grounded decisions.</li><li>Contribute to a culture of statistical rigor, clarity, and curiosity.</li></ul><h3>What We Value:</h3><ul><li>Deep understanding of statistics, probability, and causal inference.</li><li>Comfort moving between theory and practice: from modeling assumptions to implementation details.</li><li>Clear, precise communication of methods, assumptions, and results.</li><li>Patience to validate before scaling, and rigor in separating correlation from causation.</li></ul><h3>Bonus Points:</h3><ul><li>Experience with uplift modeling.</li><li>Experience in product analytics or growth-focused DS.</li><li>Experience working with Airflow, BigQuery, dbt, or similar data infra.</li><li>Experience in training and shipping models to production.</li><li>Familiarity with basic experimentation pitfalls (SRM, novelty effects, power issues).</li></ul><h3>How We Hire:</h3><ul><li><b>Online assessment:</b> technical logic and fundamentals (Math/Calculus, Statistics, Probability, Machine Learning/Deep Learning, Code).</li><li><b>Technical interview:</b> deep dive into theory and reasoning for fraud detection (no code).</li><li><b>Cultural interview.</b></li><div>If you are not willing to take an online quiz, do not apply.</div></ul>