Sr Data Scientist (LATAM Remote)

About the position UPLabs is a dynamic venture studio dedicated to building innovative startup companies from the ground up. Our team thrives on solving complex problems, driving technological advancements, and creating impactful digital products. We're seeking an applied data scientist who ships data products as an engineer, to help us launch the next wave of AI-enabled ventures. This is a hands-on role for someone who can take a problem from data to deployed, monitored data product, with strong statistical judgment along the way. In this role, you’ll work with large-scale datasets to build scalable machine learning systems and intelligent data platforms. You’ll help define how data is stored, processed, referenced, and utilized across AI workflows and operational systems. You’ll collaborate closely with engineering, operations, and product teams to build production-grade ML solutions while contributing to architectural decisions around modern data infrastructure. This is a highly hands-on role with strong ownership and technical influence.

Responsibilities

• Apply statistical and machine learning methods to operationally meaningful problems.
• Build and refine digital twins and predictive models of physical assets, processes, and operational workflows.
• Work across hybrid data estates that span on-prem operational systems and modern cloud platforms.
• Use modern ML frameworks (PyTorch, TensorFlow) where they earn their place, and simpler tools where they don't.
• Run rigorous, reproducible experimentation using tools like MLflow.
• Work with large-scale structured and unstructured datasets in Snowflake environments
• Develop and maintain scalable data pipelines, ETL/ELT workflows, and ML infrastructure
• Design systems for storing, processing, and managing ML outputs, embeddings, and AI-generated data
• Collaborate with cross-functional teams to translate business problems into scalable data solutions

Requirements

• Hands-on experience in Data Science, Machine Learning, or ML Engineering roles in Big Data environments
• Strong applied statistics: experimental design, inference, uncertainty quantification, and a working sense of when a result is real versus an artifact of the data.
• Strong Python and SQL fluency, including comfort with modern distributed SQL engines (e.g., Trino, Spark SQL, or similar).
• Comfort working across hybrid data environments spanning on-prem operational sources and modern cloud platforms (AWS, Azure, or GCP).
• Experience with the full ML lifecycle: ingestion, transformation, feature engineering, training, evaluation, deployment, and monitoring.
• Practical experience with modern ML frameworks (PyTorch or TensorFlow) and experiment tracking tooling (MLflow or comparable).
• Strong problem-solving skills and ability to work in fast-paced startup environments
• Experience working with Snowflake in production data environments.

Nice-to-haves

• Experience delivering models as containerized services like Docker and Kubernetes
• Direct experience with digital twins or applied modeling of physical / operational systems.
• Time-series, sensor, or streaming data at production scale.
• Open lakehouse formats (Iceberg, Delta, Hudi) and table-format-aware workflows.
• Causal inference, A/B testing, or sequential evaluation
• Edge or hybrid model deployment patterns.
• Experience with Databricks or comparable platforms.

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