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Post-Doctoral Fellow - Scotiabank Centre for Customer Analytics

Post-Doctoral Fellow - Scotiabank Centre for Customer Analytics

Job Title: Post-Doctoral Fellow

Department: Scotiabank Centre for Customer Analytics

Supervision and Academic Unit: Dr. Ceren Kolsarici, Smith School of Business and Dr. Xiaodan Zhu, Engineering and Applied Sciences

Description of Area or Topic of Research:

The Smith School of Business at Queen’s University invites applications for a Post-Doctoral Fellow (PDF) position to take a leadership role in the Scotiabank Centre for Customer Analytics (SCCA). The PDF will work in collaboration with the Faculty of Engineering and Applied Sciences’ Department of Electrical and Computer Engineering (ECE) as well as with the Ingenuity Labs Research Institute. The PDF will use modern techniques in artificial intelligence, machine learning and more specifically, natural language processing to tackle research problems and advancements in the following (yet not limited to) areas under the supervision of Dr. Xiaodan Zhu and Dr. Ceren Kolsarici, their collaborators and industry partners:

  • Creating rich, interactive data and visualization tools
  • Recommendation/Inference engines optimization

This role will position the PDF to transition to industry or to a teaching role at a research institution. The PDF will conduct research in new methodologies and perform real-data exercises supported by our industry partner, Scotiabank. Ultimately, the PDF is expected to develop sharing dashboards between teams for thousands of employees for which the data currently resides in multiple sources across the enterprise. The PDF will also create and edit written materials for publications as well as give talks to engage the broader Smith School of Business Analytics & AI community and Scotiabank stakeholders. The PDF is expected to be independently motivated to execute high-quality research in a collaborative environment.

Qualifications

The successful candidate should have a PhD completed within the last 5 years in Computer Science, Machine Learning, Statistics, Operations Research, Engineering or a related field. They will ideally have familiarity with supervised and unsupervised machine learning, deep learning, natural language processing, chatbots, recommender systems, and related AI/ML and statistics literatures. Experience with both shallow NLP procedures (e.g., TF-IDF) and deep NLP procedures (e.g., RNNs, large pre-trained language models such as GPT-3) is preferred. Experience in Python is preferred.

This position will require the ability to work independently as well as in teams and to communicate effectively with a variety of project stakeholders both internal and external to Smith. The ability to communicate technical material effectively to a broad audience as well as the ability to set goals, track progress and prioritize as necessary to meet the deadlines are highly desired.

Compensation

$80,000 annually; plus consideration for relevant industry experience.

Start Date and Duration of Appointment:

January 1, 2022 for one year with the possibility of extension.

Equity Statement

EMPLOYMENT EQUITY: The University invites applications from all qualified individuals.  Queen's is strongly committed to employment equity, diversity, and inclusion in the workplace and encourages applications from Black, racialized/visible minority and Indigenous/Aboriginal people, women, persons with disabilities, and 2SLGBTQ+ persons.

ACCOMMODATION IN THE WORKPLACE: The University has policies in place to support its employees with disabilities, including an Accommodation in the Workplace Policy and a policy on the provision of job accommodations that take into account an employee's accessibility needs due to disability. The University will provide support in its recruitment processes to applicants with disabilities, including accommodation that takes into account an applicant's accessibility needs. If you require accommodation during the interview process, please contact Daniel McBride at daniel.mcbride@queensu.ca.

How to Apply

Deadline for applications:

November 01, 2021

Applicants should submit:

  1. Curriculum vitae
  2. Ph.D. thesis, thesis papers, or writing sample if available
  3. Cover letter, including a brief description of research interests and how this position will help advance your career goals.
  4. Graduate transcripts
  5. Two recommendation letters: have referees email daniel.mcbride@queensu.ca directly.

First review of applications will be on November 1, 2021. Applications after this date will be considered on a rolling basis.

Submit your application to:

Daniel McBride
Associate Director
Scotiabank Centre for Customer Analytics
daniel.mcbride@queensu.ca