Data Scientist

Tryolabs · Natural Language Processing · Computer Vision · 16 days ago
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Location
In office
Employment
Full-Time

Confirmed on the employer's own hiring board on Nov 7, 2026. First seen by Alion on Sep 22, 2026.

Overview
Company
Impact
Profile match

Tryolabs

Headquartered in Montevideo, Uruguay, Tryolabs is a specialized artificial intelligence and machine learning consulting firm. The company provides custom end-to-end digital solutions, including computer vision, natural language processing, data engineering, and agentic workflows. By bridging strategic business goals with production-grade software engineering, it enables global enterprises to de-risk complex AI initiatives and optimize operational efficiency.
tryolabs.com · HQ: Montevideo, Uruguay · Natural Language Processing · Data Engineering & Migration Services · Computer Vision · AI Consulting & Integration · Est. 2009
Tech stack of Tryolabs
You'll work at a technically advanced airline, solving real and complex problems alongside engineering, data, and product teams.

About the role

The Data Scientist is an individual contributor who exists to enable product, business, and operational decisions by designing, developing, and integrating end-to-end analytical and machine learning solutions, ensuring methodological rigor, traceability, and responsible use of data to generate verifiable impact within a squad, domain, touchpoint, or journey. You'll operate at the intersection of analytics, engineering, and business, turning data into actionable decisions and high-impact solutions.

What we're looking for

  • Experience in numerical optimization problems (e.g., linear programming, integer programming), and hands-on use of solvers (SciPy, Gurobi, CPLEX, OR-Tools, etc.).
  • Analytical framing and business understanding
  • Statistics, inference, and experimentation
  • Programming and development of data solutions
  • Modeling and machine learning
  • Data management and understanding
  • Definition of product and business metrics
  • Governed reporting design
  • Data quality and validation

Nice to have

  • LLM supervision and evaluation
  • Applied experimentation and inference
  • Metric design and evaluation
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