Analista Motor de Riesgo

This is a fully remote position, open to applicants in Chile.

📋 Description

• Manage the decision engine efficiently to ensure the proper implementation and validation of current risk policies.

• Generate, automate, and maintain dashboards and reports that facilitate the control of the institution's risk appetite and detailed monitoring of executed pilots.

• Configure, maintain, and manage the technical environment of the engine, ensuring its stability and continuous proper functioning.

• Ensure the accurate technical implementation of the designed credit policies and rules within the system.

• Build and maintain visualization panels (dashboards) and executive reports focused on monitoring and tracking Tenpo's risk appetite.

• Conduct operational and technical follow-ups on various pilots (A/B testing, champion/challenger strategies).

• Proactively identify execution errors in the parameterized rules and raise early alerts in response to operational deviations.

• Make projections and impact assessments on risk appetite in light of the potential implementation of new rules or changes in credit policies.

⛳️ Requirements

• Professional degree in Commercial Engineering, Industrial Civil Engineering, Mathematical Civil Engineering, Statistics, Economics, Computer Science, or a related field with a solid quantitative foundation.

• Between 2 and 4 years of experience in credit risk roles, data science applied to credit, portfolio analysis, or equivalent positions in financial sector entities.

• Advanced SQL skills for querying large volumes of credit data, cohort analysis, and feature construction.

• Experience with BigQuery or Spark for processing and analyzing data at scale, in data-driven environments typical of the fintech industry.

• Advanced Excel or Google Sheets for portfolio analysis, risk matrix construction, and executive presentation preparation.

• Proficiency in Looker, Power BI, or Tableau for designing and maintaining portfolio monitoring dashboards.

• Knowledge of CMF regulations applicable to credit risk: portfolio classification and provision establishment.

• Advanced technical knowledge of Python (pandas, NumPy, scikit-learn) for portfolio analysis, scoring model development, and report automation.

• Statistical modeling applied to credit: logistic regression, decision trees, gradient boosting (XGBoost, LightGBM), and evaluation metrics (Gini, KS, AUC-ROC).

🏝️ Benefits

• Annual budget for self-managed training.

• Remote work.

• Day off on your birthday.

• Half-day off for children's or parents' birthdays.

• Short Fridays (every Friday we finish at 14:00).

• Work from anywhere (Work from wherever you want! Just remember to have a good internet connection).