Senior Data Scientist with Causal Inference

About the Position

We are looking for a Senior Data Scientist / Applied Scientist to develop and validate robust econometric and causal inference methodologies for cross product price elasticity and to translate scientific findings into actionable business decisions. The role centers on methodological rigor, identification and bias assessment, and the clear communication of complex results to stakeholders, while strengthening the client's internal pricing science capabilities and extending its Elasticity Playbook.

About the Project

The client is expanding its internal Price Elasticity Framework to model cross product price elasticity across ancillary products, capturing substitution, complementarity, cannibalization, and portfolio interactions so that total ancillary revenue is optimized rather than individual products in isolation. The work is grounded in causal inference and econometrics, using Python (with libraries such as statsmodels and pyfixest), SQL, and BigQuery under rigorous experimentation and validation practices. This is a science led role in which the person co designs methodology directly with the client's science experts.

About the Team

You will collaborate closely with internal science experts and stakeholders in a science led environment. The work involves methodological co design, analytical review, and knowledge transfer across pricing and business teams.

Responsibilities

• Develop cross product price elasticity methodologies and causal econometric models.
• Analyze substitution, complementarity, cannibalization, and portfolio interactions across ancillary products.
• Define assumptions, assess identification bias, validate results, and extend the client's Elasticity Playbook.
• Design experiments and interpret results using a causal framework to support decision making.
• Build proof of concept use cases for ancillary products and review analytical implementations.
• Advise stakeholders, communicate complex findings clearly, and transfer knowledge to client teams.

Requirements

• PhD in Econometrics, Statistics, Economics, Mathematics, or a related field (mandatory for this position).
• Proven experience in causal inference, econometrics, pricing science, demand modeling, or cross price elasticity.
• Advanced Python skills for scientific work; R is desirable.
• Strong command of experimentation, including A/B testing, panel data, and quasi experimental methods.
• Solid experience in statistical testing, bias assessment, and model validation.
• SQL and BigQuery skills for data access and analysis.
• Familiarity with statsmodels, pyfixest, scikit learn, MLflow, and Docker is desirable.
• Strong communication and research skills, with the ability to influence business and product decisions.
• Fluency in English, the team's working and documentation language; Spanish is desirable for regional collaboration.