Senior Data Scientist – Risk Modeling (Senior Data Scientist – Modelado de Riesgos) - Hybrid

<div class="content-intro"><p><strong>Clara is the leading platform in Latin America for companies to execute and manage every payment operation.</strong> More than 50,000 leading and fast-growing businesses run on our corporate cards, bill pay, financing and B2B payments, and in 2025 the Financial Times named Clara the region’s fastest-growing company. We’re passionate about helping every company we work with become more effective—and, through that, raising the region’s competitiveness. We’re ~400 people, backed by Notable Capital, Coatue, Kaszek, monashees, DST Global, ICONIQ, General Catalyst and Goldman Sachs, among others.</p></div><div>What you'll do<br>We're looking for a <strong>Senior Data Scientist – Risk Modeling</strong> to join Clara’s Risk Data Science team. In this role, you will combine advanced analytics, machine learning, and credit risk expertise to develop and improve models and strategies that support underwriting, portfolio management, and risk decision-making across Clara’s markets.<br>You will work closely with <strong>Risk, Data, Engineering, Finance, and Operations</strong>, taking analytical problems from exploration and model development through validation, monitoring, and business implementation.<br>Your responsibilities will include:</div> <ul> <li><strong>Develop credit risk models:</strong> Design, build, validate, and maintain predictive models for <strong>credit origination, behavioral risk, portfolio management, and other risk use cases</strong>.</li> <li><strong>Own the modeling lifecycle:</strong> Work across the full model lifecycle, including problem definition, population and target construction, feature engineering, model development, validation, backtesting, calibration, monitoring, and recalibration.</li> <li><strong>Drive advanced risk analytics:</strong> Use <strong>SQL and Python</strong> to explore large datasets, identify portfolio trends, analyze delinquency and losses, and translate findings into actionable risk strategies.</li> <li><strong>Strengthen credit decisioning:</strong> Support the development and optimization of underwriting strategies, score cutoffs, credit limits, segmentation, and portfolio management policies.</li> <li><strong>Monitor model and portfolio performance:</strong> Build monitoring frameworks to track model discrimination, calibration, stability, data drift, portfolio trends, vintages, roll rates, delinquency, and other key risk indicators.</li> <li><strong>Improve data and modeling quality:</strong> Validate data sources, implement data quality controls, assess feature stability, and identify potential issues such as leakage, selection bias, or population drift.</li> <li><strong>Work with rejected and unobserved populations:</strong> Contribute to methodologies for addressing <strong>reject inference, selection bias, thin-file populations, and limited performance information</strong> where relevant.</li> <li><strong>Develop in a modern ML environment:</strong> Use <strong>Databricks, MLflow, GitHub, Python, SQL, scikit-learn</strong>, and other appropriate modeling tools to build reproducible and well-documented analytical solutions.</li> <li><strong>Support model implementation:</strong> Collaborate with Data and Engineering teams to ensure models developed by Risk Data Science can be reliably deployed and integrated into business decision flows.</li> <li><strong>Translate analytics into business decisions:</strong> Communicate complex analytical findings clearly to Risk leadership and non-technical stakeholders and help turn model outputs into actionable business strategies.</li> <li><strong>Contribute to Risk Analytics standards:</strong> Help build scalable methodologies for model development, validation, monitoring, documentation, and governance across <strong>Mexico, Brazil, and Colombia</strong>.</li> </ul> <div><br><br>Who you are<br><em>We’re looking for someone who meets the minimum requirements to be considered for the role. Preferred qualifications are a bonus, not a requirement.</em><br>Must haves</div> <ul> <li><strong>4–6+ years of experience</strong> in Data Science, Risk Analytics, Credit Risk, or related analytical roles.</li> <li>At least <strong>2 years of hands-on experience developing or validating credit risk models</strong> or other predictive risk models.</li> <li>Strong proficiency in <strong>Python and SQL</strong> for data manipulation, statistical analysis, and model development.</li> <li>Experience working with <strong>Databricks</strong> or similar cloud-based analytics platforms.</li> <li>Experience developing predictive models using libraries such as <strong>scikit-learn, LightGBM/XGBoost, PyTorch</strong>, or equivalent tools.</li> <li>Understanding of the <strong>full model lifecycle</strong>, including development, validation, backtesting, monitoring, recalibration, and documentation.</li> <li>Strong understanding of <strong>credit risk analytics</strong>, including concepts such as: <ul> <li>delinquency and default;</li> <li>vintage analysis;</li> <li>roll rates;</li> <li>bad rates;</li> <li>portfolio performance;</li> <li>score discrimination and calibration;</li> <li>population and model stability.</li> </ul> </li> <li>Experience working