Senior SQL Machine Learning Data Scientist

Join the Fintech revolution and build the future of finance in Mexico!
We are the leading financial technology company in Mexico, empowering more than 70,000 clients to achieve their dreams. Our mission is to empower the country's small and medium-sized enterprises with innovative solutions (financing, credit card and payments) to overcome their challenges and turn them into the engines of economic growth. We aspire to be the ideal ally of entrepreneurs, contributing to the development of the community, the country and the planet.

Your Mission, Should You Choose to Accept It:
The primary goal of the Senior Data Scientist is to develop, implement, and maintain machine learning or AI models and algorithms to enhance underwriting processes and customer management strategies. This role involves continuous improvement and experimentation with models, monitoring their performance, and providing data-driven insights to support business decisions. Model Development and Implementation: Design, develop, and implement machine learning models and algorithms for underwriting and customer management.
Design and conduct experiments to enhance existing models, ensuring their accuracy and reliability through rigorous testing, validation, and benchmarking.
Performance Monitoring and Retraining: Implement monitoring systems to track the performance and quality of models, regularly reviewing and retraining them based on evolving data patterns and business requirements.
A Bachelor's degree in a quantitative field (e.g., Actuary, Mathematics, Physics, Statistics, Computer Science, Data Science, or related field) Plus 5 years of experience performing data analytics.
Proven experience in Data Science (at least 3 years).
Strong SQL knowledge. Production programming experience in Python (Class oriented) and Pandas library. Cloud Services Knowledge (Preferrable AWS).
Statistics fundamentals: hypothesis testing, parametric models (e.g. generalized linear model, logistic model, time series model, etc), hierarchical models, A/B testing, statistical sampling methods, experiment design, simulation.