Research Scientist
Altus Assessments, Canada
Job Description
Our Purpose:
We are here to create a world served by exceptional professionals. Our products (Casper, Snapshot, and Duet) help higher education institutions to look beyond book smarts, seeing a clearer, more holistic view of applicants. It’s used by 360+ academic partners in Canada, US, Australia (and growing!) with 150,000+ applicants taking the test each year, and covers 90% of all US medical school applicants. We also host the Admissions Summit, an annual 2-day conference, and power the Alo Grant, an annual $100,000 research fund.
The Role
Are you interested in engaging in diverse research programs in the field of assessment and admissions? We are looking for a research scientist to support high calibre research projects and to lead independent research. You will have the opportunity to make a meaningful impact on academic admissions across professions by helping us learn how our partners can select better professionals. You will engage in all stages of research including study design, instrument evaluation, data cleaning and analysis, report and publication writing, and presentations. Specifically, you will be responsible for: providing expert guidance in statistical methods for education and assessment, using R, R Markdown and Looker to quickly deliver professional reports, and enhancing the analytical capacity of the team by developing analysis standards.
This is a full-time role reporting to the Research Manager, ideally located in Ontario but open to other parts of Canada. We are a remote-first company but will return to a limited office presence in Toronto post-Covid to accommodate employees that want/need an office.
You will:
- Conduct all aspects of research including: idea generation, instrument development, study design, data analysis, report and publication writing, and presentations)
- Define metrics and key performance indicators; document data streams; and help develop and maintain data protocols and procedural manuals.
- Create data visualizations, performing statistical analyses of structured and unstructured data sets and develop data strategies using R, Rmarkdown, Looker.
- Work with real-world datasets to design and execute quantitative analyses that extract actionable insights to directly address client needs and questions
In 12 months:
- You have earned the respect of the Altus team for your endless curiosity and creative problem-solving - always with another question, and the drive to find the answer quickly
- You have earned the respect and trust of our academic partner programs who see you as deeply knowledgeable about measurement and assessment in education and statistics
- You are known for your speed of execution and will always be looking for creative ways to accelerate academic research
- Your peers look to you for deep insights and perspective on problems - how we might analyze data differently or present data differently to help our partner programs or create better products
- You are able to take research findings and translate them into different media (abstracts, academic papers, etc.) in a way that is easy to understand by all audiences
- You are known to be able to juggle the workload of multiple simultaneous projects - using project management and tools to ensure project progress smoothly
- You have earned the trust of your colleagues as being very reliable - if you say something is getting done, it gets done!
- You are on your way to being an emerging thought leader in educational research through submitting academic papers along with attending and speaking at conferences
Requirements
What will help you in your role:
- A Masters or Doctoral degree in Education, Measurement and Assessment, Statistics or a related field and 3+ years of experience solving psychometric and statistical problems.
- Expertise performing statistical analyses, writing reports and creating data visualizations using R and R Markdown.
- Expertise determining the most appropriate statistical methodologies - this position will require the application of multiple approaches because of the diverse questions we need to answer.
- Experience modelling data and practical decision-making using the following methods:
- Multiple linear, non-linear and logistic regression
- Parametric and non-parametric analyses of variance
- Multi-objective constraint-based optimization
- Multiple criteria decision making
- Measure of assessment reliability and validity including:
- Confirmatory and exploratory factor analysis
- Item Response Theory
- Generalizability
- Experience in communicating information for both an academic audience (