Department: Consulting Location: Montreal, Canada - Head Office

Description

We’re looking for late PhD-level Applied Research Fellows to join us for 3-4 months. This role is designed for students whose fellowship is funded by their university or an external academic programThe 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 Personify Health

"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 Mila AI 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.

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.