Responsibilities
- Develop and implement machine learning models to address practical challenges in financial crime and risk management.
- Tackle use cases including fraud identification, chargeback forecasting, anomaly detection, identity validation, and transaction surveillance.
- Convert unclear business needs into defined, measurable machine learning outcomes.
- Collaborate with Product and Operations teams to establish project requirements, performance indicators, and decision-making processes.
- Examine large-scale datasets to detect trends, potential improvements, and emerging risks.
- Create and sustain robust data workflows and model deployment systems for production environments.
- Track model effectiveness and iteratively enhance precision, stability, and business value.
- Work alongside Data Engineering and Backend Engineering teams to scale machine learning solutions.
- Contribute to advancing the organization's long-term AI and machine learning strategy.
Benefits
- Market-competitive compensation and benefits package
- Equity participation through stock options
- Performance-based discretionary bonuses
- Access to up-to-date software and hardware tools
- Collaboration with a high-performing team that fosters professional development
- Chance to contribute to the leading fintech application in Latin America
Compensation
Competitive salary, stock options, and discretionary performance bonus
Work Arrangement
On-site
Team
Collaborative environment with Product, Operations, Data Engineering, and Backend Engineering teams focused on advancing AI capabilities.
Other
Office Policy: Employees are expected to work in the office 3-4 days per week.
Not specified
