Doctoral Student in Operational Decision Making with Data Science 100%

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

The Decision Sciences and Analytics group, led by Prof. Tu Ni at ETH Zürich is seeking a doctoral student to study operational decision making with data science. This position focuses on the development of experimental and causal methods for efficient and robust evaluation of new product, policy, and technology in modern operational context of organizations. The research is connected to several industrial partners in online marketplaces.

Job description

• Develop new methods for experimental and causal evaluation that are relevant in real business
• Work with industrial partners on implementing the methods
• Write high-quality papers and publish on top-tier OM journals
• They will also have the opportunity to develop their own research questions within the broader topic of the data-driven decision making
• In addition to research activities, they will contribute to light tasks and responsibilities (e.g. teaching, lab activities)

Profile

• Excellent academic results and is motivated to produce high-quality, policy-relevant research
• Hold (or will have completed at the time of appointment) a Master's degree in statistics, operations, economics, or a closely related field
• Have excellent analytics skills: statistics, optimization, stochastic modeling, etc
• Have excellent verbal and written communication skills
• Have experience in working on real A/B testing is a plus

We offer

• The starting date is negotiable
• The position is initially offered for 18 months, with the prospect of extension for four- to five-year duration of the doctorate, subject to satisfactory progress
• We offer the opportunity to pursue a high-quality PhD at a renowned institution and to work in a dynamic and collaborative research environment
• Gross salary will be in accordance with the regulatons of ETH Zurich

We value diversity and sustainability

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us - we are consistently working towards a climate-neutral future.

> Working, teaching and research at ETH Zurich

Curious? So are we.

Further information about Department can be found on our Website. Questions regarding the position should be directed to Tu Ni, [e-mail] (no applications).