Data Scientist

About the Position

We are looking for a Data Scientist (Mid Level) to work as an individual contributor within a squad, owning analytical problems end to end, from framing the business question to building models or experiments and taking solutions into production with methodological rigor and traceability. The role sits at the intersection of analytics, engineering, and business. You will work directly with product and operations stakeholders on a daily basis. This is an execution focused role with ownership of outcomes, participation in solution design, and no people management responsibilities.

About the Project

The position sits within the Digital Operations organization, in the domain responsible for onboard services and crew management. The team turns operational and customer data into decisions by measuring the impact of onboard initiatives on indicators such as NPS, cost, food waste, and product availability, while delivering Generative AI support for cabin crew so that procedures and operational information are available in real time during operations.

Responsibilities

• Translate business and operational problems into analytical problems and convert analytical results into actionable business decisions.
• Design, develop, and integrate end to end analytical and machine learning solutions.
• Design and conduct experiments to measure the impact of new initiatives on key operational and customer indicators.
• Build and validate LLM based solutions for operational users, including output evaluation.
• Define and standardize product and business metrics in collaboration with stakeholders.
• Ensure the responsible, documented, and traceable use of data.
• Contribute to the evolution of the team's analytical capabilities and ways of working.

Requirements

• 3+ years of experience as a Data Scientist or in a similar applied analytics role.
• Strong Python skills, including Pandas, NumPy, and scikit learn, and strong SQL skills.
• Solid knowledge of statistics, including inference, experimental design, A/B testing, and applied causal inference.
• Experience building predictive models for decision making, from exploration through deployment.
• Experience working with a cloud data warehouse. BigQuery is preferred, although equivalent experience that can be readily transferred is also acceptable.
• Understanding of deployment fundamentals, including MLflow, Docker, version control, and CI/CD concepts.
• Ability to communicate effectively with both technical and non technical stakeholders and influence decisions without formal authority.
• Fluency in Spanish, with intermediate English skills for documentation and international collaboration.

Nice to Have

• Experience with Statsmodels, SciPy, Polars, or Pyfixest
• Practical experience with LLM based solutions and their evaluation
• Experience with Looker Studio or a similar BI tool for data visualization and reporting
• Experience using AI assisted development tools as part of the delivery workflow

Technologies

The technology stack includes Python and SQL on BigQuery, with Pandas, NumPy, SciPy, Statsmodels, and scikit learn for modeling, MLflow and Docker for model packaging and tracking, and Looker Studio for reporting. AI assisted development is part of the team's day to day workflow.