Principal Full Stack Data Scientist - LATAM

Sofka•Colorado Springs, CO

8h•Remote

About The Position

We are looking for a Principal Full Stack Data Scientist who can work with equal depth in data engineering and machine learning. This role requires someone capable of taking a model from initial development to production autonomously, without relying on intermediaries. The ideal candidate will deliver end-to-end solutions, including designing pipelines, training models, and deploying and monitoring them in production. A key differentiator for this role is the active use of Generative AI tools as part of the daily workflow, leveraging them to deliver more and better results. This is a high-impact position with total technical autonomy, ideal for a professional who takes complete ownership of a system from data ingestion to live inference and uses AI to enhance their work daily.

Requirements

Degree in Systems Engineering, Computer Science, Data Science, Mathematics, Statistics, or related fields with a strong background in engineering and software development.
5+ years of proven experience leading end-to-end ML projects, from raw data to production systems.
Hands-on experience designing data architectures and ETL/ELT pipelines, with a history of real deployments outside of notebook environments.
Daily use of GenAI-based productivity tools is a requirement.
Advanced proficiency in Python and core ML libraries (scikit-learn, PyTorch, or TensorFlow).
Solid data engineering fundamentals: pipeline design, data modeling, and ETL/ELT processes.
Practical experience with data engineering tools: dbt, Airflow, Spark, or equivalents.
Proficiency in SQL.
Experience deploying and monitoring models in production (Docker, FastAPI, cloud ML services).
Active and professional use of Generative AI tools in the daily workflow (Copilot, Cursor, LLM-based agents, prompt engineering).
Ability to work completely autonomously across the entire data and ML stack.
Advanced English (C1 or higher) for communication with the team and stakeholders.

Nice To Haves

Experience with fine-tuning LLMs, RAG architectures, or building GenAI-powered features.
Familiarity with MLOps tools (MLflow, Weights & Biases, SageMaker, Vertex AI).
Experience with cloud platforms (AWS, GCP, or Azure).
Experience with streaming data or real-time inference pipelines.

Responsibilities

Design and build data pipelines that feed ML training and inference workflows.
Develop, evaluate, and iterate on machine learning and statistical models oriented towards business solutions.
Manage the complete model lifecycle: development, validation, deployment, and monitoring.
Integrate GenAI tools (LLMs, copilots, agents) into the daily development workflow to accelerate deliveries.
Deploy models in production environments, ensuring reliability, performance, and system maintenance.
Collaborate with stakeholders to translate complex business problems into functional data science solutions.
Document work with sufficient clarity for it to be maintained and extended by the team.