• Stori is a fast-growing, venture-backed financial technology company, on a mission to democratize credit access for 400 million underbanked LatAm consumers. Stori currently operates in Mexico and has a global team with offices in Arlington Virginia, Mexico City, and China. As one of the top digital banks in Mexico with more than two million applicants for our credit card product since launching, we have quickly made our mark.
• Stori is one of the top-funded startups in the region with US$50 million raised to date. We are backed by top global venture capital funds, such as Lightspeed Venture Partners, Vision Plus Capital, BAI Capital and Source Code Capital; who have successfully invested in startups such as Affirm, Alibaba’s Ant Financial, Snapchat, and TikTok.
• Stori has a standout founder team among fintech startups, leveraging 100+ years of accumulated experience in consumer finance, banking and technology across Mastercard, Intel, Capital One, Morgan Stanley, GE Capital, and HSBC in the U.S., Mexico and Asia. The team has launched and managed many multi-million-customer credit card products globally, providing a wide breadth of experience and knowledge to our team.
• Stori welcomes diversity of background, experience and thinking. Storians are passionate about our mission and take pride in the products we build. Our culture thrives off of a flat structure and an inclusive environment where all of our employees can be their authentic selves, with boundless opportunities for professional growth.
• Bachelor degree or higher in statistics, mathematics, physics, economics, or other analytical or quantitative discipline.
• 5+ years in model development or model governance experience in data analysis, credit analysis, and/or modeling areas post bachelor degree
• 3+ working experience with the applications of machine learning modeling, predictive modeling. Knowledge of different methodologies and technologies in machine learning modeling. Working experience of model scoring platform and implementation/execution of model.
• Great quantitative and analytical skills; ability to derive patterns, trends and insights, and perform risk/reward trade-off analysis. Strong SQL, Python, and AWS skills
• Ability to champion organizational agenda and initiatives with executive audiences. Integrate/connect team initiatives, actions and culture in support of broader vision and credit risk management goals.
  
  

The role will be responsible for governing the quality assurance and compliance of decision-making models at Stori, including but not limited to machine learning, statistical, and financial models. The role ensures models adhere to quality requirements, regulatory standards and align with business objectives.

• Model Governance, Policy Development, and Compliance
  
  

Establish and enforce policies, guidelines, and standards for model design, development, validation, and documentation, ensuring all models adhere to internal and regulatory requirements and internal model risk frameworks. Lead governance activities to assess model risk, operationalize governance policies, and ensure compliance with different requirements.

• Model Development and Technical Implementation
  
  

Oversee Model Development Procedures, Make Sure Key Model Development Stages Were Covered With Best Practices Applied Across The Entire Lifecycle. Key Stages Include By Not Limited To

• Data Preparation and Feature Engineering: Gather, clean, and engineer datasets used in model development, ensuring data quality and consistency.
• Model Design and Calibration: Develop and calibrate models using industry-standard techniques such as logistic regression, decision trees, time series analysis, and machine learning algorithms where applicable.
• Performance Evaluation and Validation: Perform rigorous model testing, including back-testing, scenario analysis, and sensitivity analysis to ensure robustness under various conditions.
• Data usages and Model implementation: Document clear data lineage, data usages and data owners for the data sources of the model. Ensure the model implementation follows the same data usages as model development. Perform rigorous model production execution monitoring pre and post model deployment.
• Technical Tools and Proficiency
  
  

Expert-level Proficiency With

• Statistical and Modeling Software: Python (required), R, SAS, MATLAB for statistical analysis, and SQL for data extraction and management.
• Machine Learning Frameworks: Familiarity with machine learning libraries, such as Scikit-learn, TensorFlow, or XGBoost, to implement advanced modeling techniques.
• Data Visualization and Documentation Tools: Proficiency with tools like Tableau, Power BI, and Jupyter Notebooks to document findings and communicate model insights.
• Version Control Systems: Experience with Git or other version control systems to manage codebases and ensure reproducibility of model development processes.
• Model Performance Tracking
• Work with model developers and model owners to standardize the model performance tracking system. Enforce the implementation of model performance tracking for each model.
• Organize the model performance reporting on a regular basis. Document key conclusions and findings. Issue follow ups to model developers to address critical findings and ensure the issues are addressed within the required time window.
• Cross-Functional Collaboration and Model Documentation
  

Collaborate with data science, IT, and business teams to ensure consistent model standards and alignment with business goals.

Maintain high-quality documentation across model development stages, including business requirements, design specifications, technical methodologies, and validation results for audit and compliance purposes.