Data Scientist, Applied AI - Remote

Overview

Azumo is currently looking for a highly motivated Data Scientist / Machine Learning Engineer to develop and enhance our data and analytics infrastructure. The position is FULLY REMOTE, based in Latin America.

In Short

• Collaborate with a dynamic team on big data analytics and applied AI.
• Design and implement advanced machine learning and deep learning models.
• Proficiency in Python and experience with production-level projects.
• Work with CNNs and Transformers, particularly in the Generative AI space.
• Productionize ML/DL models and integrate into data pipelines.
• Ingest, clean, and transform large datasets using various libraries.
• Automate workflows with orchestration tools like Airflow.
• Write production-grade Python code following best practices.
• Collaborate in Agile ceremonies and communicate results clearly.
• Participate in professional growth and community initiatives.

Requirements

• Strong background in Data Science and Machine Learning.
• Experience with deep learning frameworks, especially PyTorch.
• Familiarity with Docker, Kubernetes, and CI/CD practices.
• Knowledge of data manipulation libraries like pandas and NumPy.
• Ability to monitor and improve model performance in production.
• Experience with Agile methodologies and tools like JIRA.
• Strong communication skills in English.
• Interest in philanthropy and open-source initiatives.
• Willingness to learn and adapt to new technologies.
• Ability to work remotely and collaborate with a global team.

Benefits

• Opportunity for professional and personal growth.
• Work in a collaborative and innovative environment.
• Engagement in community service and open-source projects.
• Flexible working hours and remote work opportunities.
• Access to the latest technologies and tools in data science.
• Support for career development and skill enhancement.
• Dynamic team with talented professionals in the field.
• Challenging projects that drive digital transformation.
• Encouragement of knowledge sharing and continuous learning.
• Participation in a culture that values excellence.