Fraud Risk Model Sr Manager

About the role

Bamboohr is a Mexican fintech company that provides credit solutions to small and medium-sized businesses. The Fraud Risk Model Sr Manager will lead the design, development, and deployment of statistical models aimed at identifying and preventing fraudulent activity within the company's lending platform. This senior-level position manages a dedicated team of data scientists and analysts who build machine learning and rule-based systems to flag suspicious transactions and applications while ensuring models remain accurate and up to date.

Key facts

What you'll do

Lead the end-to-end lifecycle of fraud risk models from ideation through deployment and ongoing monitoring

Manage a team of data scientists and analysts responsible for building predictive models for fraud detection

Collaborate with the risk and compliance teams to define fraud thresholds and acceptable risk tolerance levels

Design and implement machine learning pipelines that process transaction and application data in real time

Conduct root cause analysis on fraud incidents to identify vulnerabilities in the lending workflow

Present model performance metrics and fraud trend reports to senior leadership on a regular basis

Partner with the product team to integrate fraud risk scores into the customer decisioning engine

Develop and maintain comprehensive documentation for all models, including methodology, assumptions, and validation results

Oversee the back-testing and validation of existing fraud models to ensure continued accuracy and reliability

Coordinate with external auditors and regulators during model risk assessment reviews and examinations

Evaluate new data sources and feature engineering opportunities to improve the predictive power of fraud models

Establish best practices and standards for model governance, reproducibility, and peer review across the analytics team

Monitor model drift and performance degradation signals to trigger timely retraining and recalibration of fraud detection systems

Ensure all models are documented with clear explanations of feature importance and decision logic for audit purposes

Requirements

Minimum of seven years of experience in data science, risk modeling, or a closely related field

Proven track record of managing a team of data scientists or analysts in a production environment

Strong understanding of fraud detection methodologies, including supervised and unsupervised machine learning techniques

Experience working with lending or financial services data, including transaction and credit application datasets

Proficiency in Python or R for statistical modeling and data analysis

Familiarity with SQL and big data technologies for processing large-scale datasets

Excellent communication skills with the ability to present complex model concepts to non-technical stakeholders

Bachelor's degree in a quantitative field such as statistics, mathematics, economics, or computer science

Experience with model risk management frameworks and regulatory expectations in the financial services industry

Demonstrated ability to deliver models on time within a fast-paced fintech environment while maintaining high quality standards

Knowledge of model validation techniques including back-testing, stress testing, and challenger model development

Familiarity with model monitoring tools and dashboards for tracking performance metrics in production environments

Nice to have

Experience with gradient boosting frameworks such as XGBoost or LightGBM for classification tasks

Familiarity with cloud-based data platforms like AWS or GCP for model hosting and data processing

Prior experience working in a Mexican fintech or financial institution

Knowledge of graph-based analytics and network analysis for detecting organized fraud rings

Exposure to regulatory compliance requirements specific to the Mexican financial services sector

Skills & tools

Python for statistical modeling and machine learning development

R for advanced statistical analysis and visualization

SQL for querying and manipulating large relational databases

Scikit-learn and similar libraries for building classification and anomaly detection models

Jupyter Notebook and version control with Git for collaborative model development

Tableau or similar BI tools for creating fraud trend dashboards

Airflow or similar workflow orchestration tools for managing model training pipelines

Practical notes

This role is based in CDMX, Miguel Hidalgo at the Bamboohr office

The position reports to the head of risk or a similarly senior leader within the organization

The team operates in a fast-paced environment with regular deadlines tied to product releases and regulatory updates

Candidates should expect to participate in cross-functional meetings with risk, compliance, product, and engineering stakeholders throughout the week

Occasional travel to other Bamboohr offices or industry conferences may be required as part of professional development