Description:

Lead forecasting initiatives by designing and implementing advanced time series models to predict sales and behavioral trends for thousands of customers.
Develop scalable machine learning solutions for real-time decision-making across risk management, fraud detection, and product personalization.
Collaborate with product managers, engineers, and business stakeholders to translate complex data challenges into actionable insights.
Drive innovation in fintech applications by experimenting with cutting-edge approaches like deep learning architectures and probabilistic forecasting.
Ensure compliance and transparency in model development, aligning with industry regulations and ethical standards.
Shape the data science strategy by identifying opportunities for predictive modeling to unlock new value streams.

Requirements:

At least 5 years of experience with machine and deep learning in a practical setting.
Good understanding of fintech products and risk management to interpret business data effectively.
Strong foundation in probability, statistics, and econometrics.
Expertise in time series forecasting methods (ARIMA, SARIMA, etc.) and machine/deep learning techniques (RNNs, LSTMs, etc.).
Proficiency in ML/DL frameworks in Python (Tensorflow, PyTorch, Scikit-learn).
Experience with explainable AI in the context of Deep Learning Forecasting methods.
Strong stakeholder management skills and ability to communicate findings in a non-technical way.

Opportunity to work in a data-first company with a mission to support small and medium businesses in Latin America.
Collaborate with a tight-knit team from leading organizations like Google, Amazon, and J.P. Morgan.
Engage in innovative projects that drive critical business decisions and financial opportunities.

Skills