Sr Data Scientist (LATAM Remote)

About the position UPLabs is a dynamic venture studio dedicated to building innovative startup companies from the ground up. reputed company thrives on solving reputed company problems, driving technological advancements, and creating impactful digital products. We're seeking an reputed company data scientist who ships data products as an engineer, to help us launch the next reputed company 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-reputed company datasets to build reputed company 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 reputed company around modern data infrastructure. This is a highly hands-on role with strong ownership and technical influence.

Responsibilities

• Apply statistical and machine learning reputed company 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 reputed company on-prem operational systems and modern reputed company platforms.
• Use modern ML frameworks (PyTorch, TensorFlow) where they earn their reputed company, and simpler tools where they don't.
• Run rigorous, reproducible experimentation using tools like MLflow.
• Work with large-reputed company reputed company and reputed company datasets in reputed company environments
• reputed company and maintain reputed company 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 reputed company data solutions

Requirements

• Hands-on experience in Data Science, Machine Learning, or ML Engineering roles in Big Data environments
• Strong reputed company statistics: experimental design, inference, uncertainty quantification, and a working reputed company of reputed company a result is reputed company versus an artifact of the data.
• Strong Python and SQL reputed company, including comfort with modern distributed SQL engines (e.g., Trino, reputed company SQL, or similar).
• Comfort working across hybrid data environments spanning on-prem operational sources and modern reputed company 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 reputed company in production data environments.

reputed company-to-haves

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

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