Responsibilities:

• Lead and manage the data science team for the risk squad
• Develop strategic goals and objectives for the team.
• Represent the data science function at the senior leadership level.

Requirements:

• Extensive experience managing large technical teams and projects.
• ​​Extensive experience deploying models into production.
• Extensive experience developing, managing and monitoring risk models, including risk scoring, classification, rating, propension and provision models.
• Proven ability to develop strategic goals and drive technical innovation.
• Strong leadership presence and ability to represent the data science function at the senior leadership level.

Non-negotiable Requirements:

1. Candidates must be either Actuaries or Computer Scientists. Regardless of their primary background, they must have a solid understanding of computer science.
2. Fintech Experience: Prior experience in the fintech industry is essential.

Preferred Qualifications:

1. Country-Specific Fiscal Knowledge: Candidates from the following countries will have a significant advantage if they possess basic fiscal knowledge relevant to their country:
   • Mexico: Basic knowledge of the Mexican Fiscal SAT.
   • Brazil: Basic knowledge of Brazilian fiscal regulations.
   • Colombia: Basic knowledge of Colombian fiscal regulations.
2. Finance Audit Experience: Candidates must have lived through a finance audit and understand the processes involved.
3. Understanding of Financial Conciliation: Strong understanding of financial conciliation processes.
4. Experience in Credit Card and Bill Pay: Prior experience with credit card and bill pay systems will be a plus.
5. Risk Modeling in Latin America: Experience in risk modeling specific to the Latin American market is highly desirable.
6. Open Mindset: Candidates should have an open mind and be willing to create new and innovative solutions.

Additional Requirements for Data Roles in Risk:

1. Data Analysis and Visualization: Proficiency in SQL and data visualization tools (e.g., Metabase, QuickSight). Ability to analyze financial data and create actionable insights.
2. Experience with Financial Data Systems: Familiarity with financial data systems and tools, such as those used for managing transactions, auditing, and compliance.
3. Regulatory Compliance: Understanding of regulatory requirements and compliance standards in the financial sector, especially those relevant to Latin America.
4. Machine Learning and Risk Modeling: Experience with machine learning algorithms (e.g., XGBoost, CatBoost) and their application in risk modeling and fraud detection.
5. Data Integrity and Quality: Strong emphasis on maintaining data integrity and quality, especially in financial datasets.
6. Python and R Programming: Proficiency in programming languages commonly used for financial modeling and analysis, such as Python and R.
7. Stakeholder Communication: Ability to communicate complex data findings to non-technical stakeholders, including finance and executive teams.
8. Problem-Solving Skills: Strong problem-solving skills and ability to think critically about data and its application in solving business problems.
9. Agile and Lean Methodologies: Experience working in agile and lean environments, common in startups, to adapt quickly to changing business needs.