Associate Professor/Professor in Data Science Computation

Laplace Expert Ltd; Toronto; On-site; CAD 140,000 - 230,000; Full time

Job summary

University of Toronto's Department of Statistical Sciences invites an Associate Professor or Professor to advance the theoretical and methodological foundations of computation in data science. The appointee will lead international-level research, develop funded programs, and teach within statistics, computer science, and data science.

The position emphasizes scalable algorithms, simulation, optimization, and computational inference, with consideration of high-dimensional problems and theoretical

Qualifications

• PhD in Statistics, Computer Science, Data Science, or closely related field by appointment.
• Established international reputation with a sustained record of research and teaching excellence.
• Demonstrated leadership in data science computation or adjacent fields.

Responsibilities

• Lead independent research at the highest international level with a competitive externally funded program.
• Carry teaching and service responsibilities within the Department of Statistical Sciences.
• Contribute to departmental leadership and scholarly community in statistics, CS and data science.

Skills

Sampling & simulation

Computational inference

Optimization

Randomized numerical methods

Scalable algorithms

High-dimensional computation

Complexity analysis

Convergence analysis

Data science computation

Education

PhD in Statistics/CS/Data Science

Job description

Associate Professor/Professor in Data Science Computation

University of Toronto · Department of Statistical Sciences

About This Opportunity

The University of Toronto's Department of Statistical Sciences seeks an Associate Professor or Professor whose research advances the theoretical and methodological foundations of computation in data science, including scalable algorithms, simulation, optimization and computational inference.

This senior tenure-stream search focuses on the computational foundations of modern data science rather than on a single application domain. The department is seeking established scholars working on areas such as sampling and simulation, computational inference, optimization, randomized numerical methods and scalable algorithms for high-dimensional problems. Particular emphasis is placed on the statistical-computational tradeoffs behind these methods, including complexity, convergence and theoretical guarantees. Candidates must have an established international reputation and a sustained record of high-impact research and teaching. The successful appointee will be expected to lead independent research at the highest international level, maintain a competitive externally funded program, and carry teaching and service responsibilities in the Department of Statistical Sciences. The position therefore combines senior research leadership with methodological depth across statistics, computer science and data science.

data science computation computational statistics simulation optimization randomized numerical methods scalable algorithms high-dimensional problems

Level Researcher

Time Commitment Full-time

Start Date Jul 1, 2027

Duration Full-time tenure-stream appointment at Associate Professor or Professor rank

Reference Code 50282 / AJO #32804

Field Data Science Computation

Specialization Statistics & Operational Research

Organization Type University

Funding & Compensation

Amount Paid

Funding Type Salary

Funding Status Paid

Period Per Year

Requirements

Education

PhD in Statistics, Computer Science, Data Science or a closely related discipline by the time of appointment.

Experience

Established international reputation and a sustained record of research and teaching excellence in Data Science Computation or adjacent fields, evidenced by high-impact contributions and publications, major presentations, awards or other disciplinary leadership.

Technical Skills

Sampling and simulation Computational inference Optimization Randomized numerical methods Scalable algorithms High-dimensional computation Complexity analysis Convergence analysis

General Skills

Research leadership University teaching Academic service Scientific communication