Data Scientist

Type of work: Full time (M-F)- long term - contractor (USD) - Hybrid

Time zone: Argentina Standard Time

Equipment: Company-provided work laptop

English Level: Advanced, written and spoken

The contractor must be based in: Córdoba, Argentina

Company:

Office location: Córdoba, Argentina

About ID90 Travel

Did you know that one of the privileges of working for an airline is being able to fly around the world for free? At ID90 Travel, we provide the technology that makes airline employee travel possible for the world's leading airlines. We are the leading one-stop comprehensive solution for airline employee travel needs, offering exclusive rates on hotels, cruises, rental cars, and more. We are a team of travel industry insiders passionate about building modern, easy-to-use technology to solve complex challenges in travel.
We are driven by continuous experimentation, data-backed ideas, and a shared passion for innovation. Here, you’ll find high autonomy and a supportive team environment where you are empowered to take initiative, learn from every iteration, and propose impactful solutions.

Job Description

Your mission as a Data Scientist at ID90 Travel is to bridge the gap between technical execution and business goals by owning data science initiatives end-to-end. You’ll be part of our Data Team, reporting to the Data Architect and working alongside Data Engineers, while partnering with stakeholders across the business. This is a highly cross-functional role where you’ll analyze complex travel datasets, design and validate machine learning solutions, and turn data into practical recommendations and reliable production models that drive measurable value for airline employee travel needs.

Key Responsibilities

• Own Data Science Projects End-to-End
• Own data science initiatives from understanding the business problem through exploration, solution design, development, validation, and deployment.
• Work with stakeholders to turn business questions and opportunities into well-defined data problems and measurable goals.
• Choose the right approach for each problem, whether that means statistical analysis, experimentation, machine learning, forecasting, recommendations, or a simpler solution.
• Define how success will be measured and evaluate solutions against both technical performance and business outcomes.
• Partner with Engineering and Infrastructure teams to integrate data science solutions into production systems.

Analyze Data and Generate Insights

• Explore and analyze complex datasets to uncover patterns, trends, and opportunities that can help guide business decisions.
• Work with teams across the company to understand their questions and help answer them with data.
• Identify assumptions, limitations, and potential biases in the data and communicate them clearly.
• Turn analysis into practical recommendations rather than simply reporting findings.

Build Data Science Solutions

• Design, develop, and validate models and algorithms that solve real business problems, such as customer segmentation, demand forecasting, recommendations, or other use cases.
• Use appropriate statistical and machine learning techniques while balancing accuracy, complexity, maintainability, and business value.
• Build solutions that can move beyond experimentation and be used reliably in real-world scenarios.
• Document important decisions, assumptions, dependencies, and limitations so solutions can be understood and evolved over time.

Own and Evolve Existing Initiatives

• Understand and take ownership of existing data science projects, including their business purpose, technical approach, assumptions, and data dependencies.
• Adapt and extend existing solutions as business needs and goals change.
• Evaluate whether an existing approach is still appropriate and recommend when it should be improved, simplified, redesigned, or replaced.
• Help ensure that existing data science initiatives continue to provide meaningful business value over time.

Collaborate and Communicate

• Work closely with Product, Engineering, Marketing, Operations, and other teams throughout the lifecycle of data science initiatives.
• Explain findings, recommendations, assumptions, and trade-offs clearly to both technical and non-technical audiences.
• Present complex ideas in a practical and understandable way.
• Help teams make