Manage the entire journey of an AI solution, from initial problem-solving and data cleaning to building production-ready systems
Implement a wide range of algorithms—from traditional statistical optimization and predictive modeling (supervised/unsupervised) to advanced Generative AI using LLMs and Transformers
Transition models from local environments to the cloud using an engineering mindset
Work across the "Big Three" (AWS, GCP, and Azure) and leverage specialized platforms like Databricks, SageMaker, and Vertex AI to handle large-scale data science needs
Translate complex datasets into actionable business insights
Collaborate with Data Engineers to integrate models into live systems
Requirements
Master’s degree in a STEM field (Computer Science, Engineering, Mathematics, Statistics, Data Science, or related)
Recent graduates are welcome, even with limited professional experience
Proficiency in R, Python and its ecosystem (NumPy, Pandas, scikit-learn)
Familiarity with Git, virtual environments, and SQL/NoSQL databases
Understanding of core ML concepts (regression, classification, clustering, dimensionality reduction)
English at a B2 level (this will be tested during the interview)
Spanish is a plus
Tech Stack
AWS
Azure
Cloud
Google Cloud Platform
NoSQL
Numpy
Pandas
Python
Scikit-Learn
SQL
Benefits
Access our Internal Academies for 80 hours of initial training
Ongoing coaching and support for top-tier certifications (AWS, Snowflake, Google BigQuery, etc.)
Semi-annual performance reviews to discuss your achievements, new challenges, and salary adjustments based on your results