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Weekend Research Assistant Jobs in Quebec (NOW HIRING)

Cascades

Joins-toi à l'équipe d' Anne Marie Beaulieu à titre de Professionnel technique - R&D, procédé et expertises à Saint-Hubert pour façonner un avenir plus responsable. Tes avantages Parce que ...

Charles River Laboratories, Inc.

CA$17.50/hr

Job Overview As a Laboratory Assistant for our Sample Processing team located in Senneville , you ... Weekend: $2.75; * Overtime: Time and a half; * Holidays: Double time; * Annual bonus based on ...

Charles River Laboratories, Inc.

CA$19.25/hr

For 75 years, Charles River employees have worked together to assist in the discovery, development ... Weekend: $2.75; * Overtime: Time and a half; * Holidays: Double time; * Annual bonus based on ...

Charles River Laboratories, Inc.

For 75 years, Charles River employees have worked together to assist in the discovery, development ... Weekend: $2.75; * Overtime: Time and a half; * Holidays: Double time; * Annual bonus based on ...

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How much do weekend research assistant jobs pay per hour?

As of Jul 18, 2026, the average hourly pay for weekend research assistant in Quebec is $23.05, according to ZipRecruiter salary data. Most workers in this role earn between $15.38 and $26.20 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Weekend Research Assistant position, and why are they important?

To excel as a Weekend Research Assistant, you typically need strong analytical abilities, attention to detail, and a background in a relevant field such as science or social sciences, often backed by current enrollment in or completion of a degree program. Familiarity with research databases, data analysis software (such as SPSS or Excel), and good documentation practices are frequently required. Excellent time management, written communication, and independent problem-solving skills will help you stand out in this position. Mastery of these skills ensures efficient support for research projects, accurate data handling, and productive collaboration with research teams during limited weekend hours.

What is a Weekend Research Assistant job?

A Weekend Research Assistant supports research projects by collecting, analyzing, and organizing data, typically working on weekends. Responsibilities may include conducting literature reviews, summarizing findings, assisting with experiments, or managing databases. This role is common in academic, scientific, and market research fields, offering flexible part-time work. Strong analytical skills, attention to detail, and research experience are often required.

What kind of responsibilities can I expect as a Weekend Research Assistant?

As a Weekend Research Assistant, you may be responsible for tasks such as collecting and entering data, conducting literature reviews, maintaining lab equipment, and supporting ongoing experiments or studies. You'll often work independently, but regular communication with principal investigators, graduate students, or other research staff is common to ensure the integrity and progress of the research. This role often involves adhering to deadlines and managing multiple projects with precision, given the abbreviated weekend schedule. Gaining this experience can also provide valuable skills for future research or graduate-level positions.

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Weekend Research Assistant jobs near you
Applied Research Fellow

Applied Research Fellow

The Decision Lab

Montreal, QC

Other

Re-posted 19 days ago

Job description

Description
The Decision Lab is an applied research and innovation firm. We use behavioral science & AI to help ambitious organizations create a better future. We do this by working with some of the largest organizations in the world, carrying out research in priority areas, and running one of the largest publications in applied behavioral science. In the past, we have helped organizations such as the Gates Foundation, Capital One, the World Bank, and many Fortune 500s solve some of their thorniest problems using scientific thinking.

Everything we do at TDL is guided by SPICE: Socially conscious, Pragmatic, Inventive, Catalytic, and Evidence-based. You can read more about us and our core values here.

What you'll be working on
This internship is structured as an applied research placement, designed to complement graduate-level training and provide exposure to how behavioral science & AI are used outside of purely academic settings.

Many of our current projects sit at the intersection of behavioral science and AI, including human-AI interaction, algorithmic decision-making, and responsible AI. Our past work has included partnering with Mila to behaviorally optimize an AI-powered COVID-19 contact tracing app, and building Hikai, an AI-powered CBT chatbot for workplace mental health. 

We are also actively developing Artificial Populations, a platform that uses synthetic participants to run focus groups, surveys, and interviews - getting decision-ready insights in hours rather than weeks.

Fellows interested in gaining applied AI experience will find no shortage of opportunities to do so here. Interns work on clearly scoped projects under the supervision of senior staff and contribute to ongoing research and consulting initiatives like these, with an emphasis on learning-by-doing, methodological rigor, and translating academic insights into real-world contexts.

Broadly, the role involves a mix of:
Research & Analysis (approximately 60%)
  • Conducting structured literature reviews and evidence syntheses
  • Supporting experimental design, measurement strategies, and analysis plans
  • Assisting with behavioral diagnostics and research frameworks
  • Contributing to research notes, working papers, policy briefs, or public-facing research outputs
  • Participating in internal research discussions and reviews

Applied Research Translation & Consulting Exposure (approximately 40%)
  • Supporting applied research projects with public- and private-sector partners
  • Translating research findings into practical insights and recommendations
  • Preparing client-facing deliverables (reports, deck, briefs) that translate research into actionable insights for external partners
  • Observing how academic research is adapted for policy, organizational, or technological contexts

The balance between research and applied work may vary slightly depending on project needs and the intern’s academic background.
Hear From Our Fellows
Naga Thovinakere, Applied Research Fellow
Currently involved in a project with a health tech company
"TDL's fellowship program is a great fit for anyone wanting to apply scientific rigor beyond academia. You're not just learning about behavioral science in the abstract - you're getting exposure on how it is used to inform product decisions on actual consulting engagements. If you care deeply about methodological rigor but also want your work to drive meaningful outcomes, you'll find this experience especially rewarding."

Maya Low, Applied Research Fellow
Currently involved in a project with an AI research institute
“This fellowship is a great opportunity for PhD students looking to broaden their horizons both in a research discipline outside their own and in a working environment different from academia. It's been eye-opening to watch research translate into practice and to imagine new paths for myself after my degree. On top of that, everyone at TDL is genuinely welcoming and happy to talk about their work!”
Where Fellows Have Made an Impact
 
Hilary Sweatman, Ph.D. (Neuroscience, McGill University)
Hilary worked with one of the largest nonprofit foundations to develop a framework for evaluating digital support tools in higher education. The project focused on how psychosocial factors shape student decision-making and engagement with ed-tech tools, with an emphasis on designing interventions that improve student outcomes.

Catalina Eneström, Ph.D. (Experimental Psychology, McGill University)
Catalina worked with a major global beauty company to investigate how sensory experiences drive consumer behavior, conducting a systematic literature review and contributing to a journal article on the psychology behind sustainable beauty habits.

Qualifications
Must-haves:
  • Current late stage PhD student in a relevant discipline (e.g., psychology, AI, economics, public policy, cognitive science, behavioral science, data science, or a related field)
  • Familiarity with behavioral science concepts and methods, such as experimental design, causal inference, behavioral interventions, or decision-making research
  • Strong curiosity about AI - a tinkering mindset that makes you want to try things and see how they work
  • Strong analytical and critical thinking skills, including the ability to conduct literature reviews, research syntheses, or empirical analysis to support research questions
  • Clear written communication skills in English, particularly for research summaries and analytical writing
  • Ability to work independently on scoped research tasks while engaging collaboratively with a research team
  • Ability & desire to leverage AI tools (e.g. Cursor, Claude Code, etc.) to accelerate writing, coding & analysis work
 
Preferred (but not required)
  • Exposure to applied research, policy analysis, or consulting-style work
  • Familiarity with AI-related topics (e.g., human–AI interaction, algorithmic decision-making, misinformation, or responsible AI), though technical expertise is not required
  • Interest in applied, policy-adjacent, or non-academic research career pathways
  • We recognize that students come from diverse academic traditions. Candidates are not expected to meet every criterion to be considered a strong fit.
  
We're flexible on start dates but ideally looking for a September start, and happy to work around your academic schedule and program commitments.


How to apply
To apply, please submit:
  • A short CV (1–2 pages) outlining your academic background and relevant experience
  • A brief statement of interest (up to 200 words) describing:
  • Your academic focus and current program
  • Your interest in applied behavioral science research
  • How this internship aligns with your training or career goals
 
Please note that this position is unpaid because it is meant to target PhD students who are already funded - either by their home program or through special programs at their home institution (e.g., graduate internship fellowships, doctoral internship programs, mobility awards). We do not accept candidates who want to work for free, as this unfairly prioritizes those with privileged backgrounds. To this end, please indicate your source of funding in your statement.

Shortlisted candidates will be invited to a brief conversation to discuss research interests, supervision structure, and alignment with current projects.

FAQs

Will publications or research outputs from the fellowship be affiliated with my home institution?
All research conducted during the fellowship is the intellectual property of The Decision Lab. That said, authorship is determined by contribution, so if you lead a project, you'll be credited as first author, and so on. We're happy to discuss specific publication or IP questions in more detail during the interview process.
What kinds of projects will I work on?
Projects vary throughout the year and span topics in behavioral science, public policy, healthcare, AI, education, sustainability, financial decision-making, and more. Because assignments depend on upcoming client engagements, we can't guarantee a specific project before the fellowship begins.
Will I collaborate with external organizations?
Yes. Many projects involve close collaboration with external partners, including nonprofits, government agencies, research institutes, foundations, and private-sector organizations. The level of interaction varies by project.
Are you flexible on the start date?
We typically run three (3) fellowship cohorts each year:
  • Early September – Mid December
  • Mid January – End of April
  • Early May – Mid August
We prefer fellows to start with one of these cohorts so they can engage with and learn alongside their peers throughout the program. However, we understand that individual circumstances vary, and we're happy to consider exceptions on a case-by-case basis.
What are the working hours?
The fellowship runs 16 weeks, at 37.5 hours per week, 9 AM–5 PM EST.
What does a typical week look like?
Once you're onboarded to your project, a typical week includes one or two internal team meetings alongside independent research, literature reviews, analysis, writing, and collaboration with your project team. Some projects also include regular meetings or presentations with partner organizations.
TDL IS AN EQUAL-OPPORTUNITY EMPLOYER
Research has found that women and people from marginalized backgrounds are more likely to feel that they’re unqualified for a position if they can’t check 100% of the boxes on the posting. So we’re telling you directly: you don’t need to be the perfect candidate in order to be a good fit for this role. If you’re a curious, communicative, and passionate person who loves to write about science, we want to hear from you.

More About The Decision Lab

OUR VALUES
As a social enterprise, we have a deep-rooted belief that better decisions make a better world. However, improving decisions is a messy and difficult thing. For this reason, we have laid out a clear set of criteria for what constitutes good work. Our approach is inspired by many of the organizations and individuals we use as role models.
We believe that a good approach to creating social impact is SPICE: Socially conscious, Pragmatic, Inventive, Catalytic and Evidence-based. We use these criteria to evaluate ourselves, our work, the clients we choose to take on and the people we make part of our team. Read more about SPICE below:

Socially conscious
We create positive and fair outcomes for individuals, organizations and societies.
The outcomes that societies want to achieve are constantly being discussed and revised, always a work in progress. They are not defined from the outset or from the outside. For these outcomes to be sustainable, they must integrate societal, environmental and economic dimensions.

Pragmatic
We develop solutions that are practical, effective and attainable.
We are deeply committed to bringing our ideals to life. To do so, we let the problem be the guide for our attention. We are agnostic regarding approaches and dispassionate in our assessment of candidate solutions. This unwavering focus on the problem allows us to employ the full range of tools at our disposal, deploying the right ones for each context.

Inventive
We develop solutions that are not constrained by the current reality.
When no existing solution is adequate to the problem at hand, we must move from curation to creation. Success in these contexts requires a commitment to exploration and an openness to inspiration.

Catalytic
We develop solutions that spark rapid transition to a new paradigm.
When we reach a tipping point, a small nudge sparks a change from one equilibrium state to another. By starting small and iterating quickly, we manage the change in a deliberate and responsible manner, ensuring that the catalytic reaction is positive when unleashed at scale. We can also help manage the journey to the tipping point, creating pre-conditions for catalytic projects to take off.

Evidence-based
We develop solutions that use evidence as a compass.
We are deeply committed to using evidence to guide our actions. We build evidence in-house through robust experimentation, and integrate our findings into a wider body of knowledge, coming from many people and many places. This cohesive landscape of insights allows us to triangulate the best course of action.