University of San Diego

What you'll study

The programme emphasises rigorous quantitative methods applied to managerial and organisational problems. Early-stage coursework covers advanced probability and statistics, econometrics, optimisation and operations research, stochastic processes, simulation, and machine learning. Students build a strong foundation in theoretical methods—such as linear and nonlinear programming, dynamic modelling and statistical inference—alongside applied topics like supply chain analytics, revenue management, forecasting and data mining.

Core elements typically include:

• Advanced econometrics and statistical inference
• Mathematical optimisation and operations research
• Deterministic and stochastic modelling
• Computational methods, simulation and programming for analytics
• Empirical methods for management research and causal inference
• Research seminars and specialist electives aligned with faculty expertise

After completing coursework and passing comprehensive or qualifying examinations, students move to the dissertation phase, undertaking original research under the supervision of faculty advisors. Doctoral students also gain teaching experience through assistantships or practicum modules and are encouraged to present at conferences and publish in peer-reviewed journals.

Entry requirements

Applicants are expected to demonstrate a strong quantitative background. Typical credentials include a relevant master’s degree or an outstanding bachelor’s degree in disciplines such as mathematics, statistics, operations research, engineering, economics or a quantitatively oriented business discipline.

• Transcripts showing substantial coursework in calculus, linear algebra, probability and statistics, and preferably real analysis or advanced mathematics.
• Evidence of research potential, such as a thesis, published papers, research assistant experience or a substantive writing sample.
• Academic references (commonly three) that speak to the candidate’s quantitative skills and research promise.
• A statement of purpose outlining research interests, fit with faculty expertise and career objectives.
• Professional CV; optional standardised test scores (e.g. GRE or GMAT) may strengthen an application where provided. Non-native English speakers must provide evidence of English language proficiency in line with university requirements.

Career prospects

Graduates from this PhD programme pursue careers in academia as tenure-track faculty or research faculty in business schools and departments of operations research or statistics. Beyond academia, alumni commonly move into senior research and analytics roles in industry, including data scientist, quantitative analyst, operations research analyst, forecasting specialist, or analytics lead in sectors such as technology, finance, healthcare and supply chain.

Other career pathways include management and strategy consulting, research positions in government and policy institutes, and leadership roles in firms that require advanced modelling and data-driven decision-making. The combination of methodological depth and applied focus prepares graduates to design and implement complex analytical solutions and to publish influential research.

Why study at University of San Diego

The University of San Diego provides a doctoral environment with close faculty mentorship, small cohort sizes and opportunities for interdisciplinary collaboration across business, engineering and data science. The faculty in management sciences and quantitative methods conduct research across optimisation, analytics, econometrics and operations, giving students access to a range of methodological approaches and applied domains.

Students benefit from the university’s connections with the San Diego business and research community, access to modern computing resources and software, and structured support for professional development, including teaching experience and conference participation. The programme’s emphasis on both theoretical rigour and practical application equips graduates to succeed in academic and industry research careers.