Data Scientist

About Queen's University

Queen’s University is the Canadian research intensive university with a transformative student learning experience. Here the employment experience is as diverse as it is interesting. We have opportunities in multiple areas of globally recognized research, faculty administration, engineering & construction, athletics & recreation, power generation, corporate shared services, and many more.
Come work with us
Job Summary
Reporting to Dr. Gunnar Blohm with and collaborating with Dr. Konrad Kording from UPenn, the Data Scientist is responsible for the sophisticated synthesis, processing, analysis, interpretation, and presentation of complex educational data and concepts relating to the UPenn Community for Rigor Project. The Data Scientist takes a lead role developing materials for scalable teaching of rigor in science and the improvement of existing materials. This position ensures sophisticated education approaches, digital artifacts, and integration with educational goals in compliance with regulations, accessibility standards and good coding practices.

The Data Scientist assists Gunnar Blohm and Konrad Kording and other team members in making strategic data-related decisions and also produces educational materials of high complexity. The materials will be produced for use by professors, researchers, students and a wide global audience. The Data Scientist collaborates with a number of Community for Rigor constituents, including the professors holding an NIH METER award. Their goal will also be to help the community for rigor be as effective as possible to improve the standards of good science at UPenn, Queens University and worldwide.
**Job Description**:
**KEY RESPONSIBILITIES**:

- Generate Units, small self-contained units of about 3-hour length each that teach one of the central concepts of rigorous science. This will often include the production of digital artifacts or widgets that make one of the principles live. This will potentially include machine learning and statistical software, including Scikit Learn, TensorFlow, PyTorch, or other related numerical software.
- Automate data workflow using Python or R programming language to ensure data is complete and accurate.
- Keeps accurate records of research data, monitor and improve quality of electronic data - develop and implement strategies, processes, tools and consistent procedures to facilitate this mandate (e.g. data tracking and checking, program validation, data monitoring, and evaluation).
- Develop and implement project plans related to the production and roll-out of relevant teaching artifacts. This will all happen in tight collaboration with the UPenn based CENTER (community for rigor)
- Coordinates teaching projects according to protocols and scientific methods as outlined by the centre leadership including implementing data collection tools and pipelines.

Page 2 of 4
- Participates in study design decisions, meetings with investigators, and presentation of scientific material to scientific and policy-oriented audiences.
- Provide advice to graduate and undergraduate students as needed, pertaining to analysis and project data sources.
- Contributes to and co-authors articles and supports drafting of scientific papers and reports.
- Other duties as required in support of HRMC objectives.

**REQUIRED QUALIFICATIONS**:

- A Master’s degree in Informatics, Biostatistics, Statistics, Computer Science and/or graduate degree in a related discipline.
- Several years (minimum 2+) of work related to education about complex computational topics.
- Strong understanding of informatics, statistics, machine learning, research methodology, and data visualization.
- Demonstrated leadership experience in producing digital content in the context of scalable teaching.
- Consideration may be given to an equivalent combination of education and experience.

**SPECIAL SKILLS**:

- Project Management Skills: Ability to identify opportunities, coordinate projects, monitor and update project tasks. Able to work simultaneously with many individuals and departments to achieve goals and ensure timelines.
- Interpersonal Skills: Demonstrated success working with interdisciplinary teams, excellent team player with an ability to interact with diverse research community across the university, and to build strong partnerships. Promotes a culture of respect and inclusion to foster a collaborative work environment.
- Communication Skills: Strong written and oral communication skills. Capable of scientific/technical writing including professional documents suitable for accreditation and quality assurance reporting, in both written and graphical form. Able to contribute to general discussions, and proactively communicate with researchers, coordinators, and staff and external partners.
- Time Management, Planning and Organizational Skills: Capable of effectively coordinating and prioritizing multiple tasks on own while remaining focused on high impac