PhD Candidate: Data-Driven Decision Making & Causal Methods

ETH Zürich; Zürich; On-site; CHF 60,000 - 70,000; Full time

Benefits offered by this job

Starting date negotiable

18-month initial appointment

PhD opportunity at ETH Zürich

Salary according to ETH Zurich rules

Job summary

ETH Zürich invites applications for a doctoral student to study operational decision making using data science. The position focuses on developing experimental and causal methods for efficient evaluation of new products, policies, and technologies in modern organizational contexts.

The research connects with industrial partners in online marketplaces. The candidate will develop new evaluation methods, work with industry partners to implement them, and publish in leading journals, while also

Qualifications

• Master's degree in statistics, operations, economics, or related field.
• Strong analytics skills across statistics, optimization, and stochastic modeling.
• Excellent verbal and written communication.
• Experience with real A/B testing is a plus.

Responsibilities

• Develop new methods for experimental and causal evaluation in real business.
• Collaborate with industrial partners to implement methods.
• Publish high-quality papers in top-tier OM journals.
• Explore research questions within data-driven decision making.
• Contribute to teaching and lab activities.

Skills

Statistics

Optimization

Stochastic modeling

Data analysis

Communication

Education

Master's degree in statistics, operations, economics, or related field

Job description

ETH Zürich invites applications for a doctoral student to study operational decision making using data science. The position focuses on developing experimental and causal methods for efficient evaluation of new products, policies, and technologies in modern organizational contexts.

The research connects with industrial partners in online marketplaces. The candidate will develop new evaluation methods, work with industry partners to implement them, and publish in leading journals, while also