other
Posted 4 days agoResearch - Internship
at Superapp
SpainOn-site
Responsibilities
- Own the exploratory research programme for Predictions: map the space from scratch using secondary research, behavioural data analysis, and primary user research.
- Conduct in-depth qualitative interviews to understand how users form and act on predictions — their mental models, decision-making heuristics, emotional responses to outcomes, and trust signals around algorithmic, community, and self-generated predictions.
- Design and field a quantitative survey to test qualitatively generated hypotheses, segment the user base on prediction-relevant attitudes and behaviours, and size opportunities.
- Build LLM-assisted analysis pipelines for transcript coding, thematic synthesis, and cross-interview pattern detection — with human validation built in.
- Analyse existing behavioural data (with Analytics team support) to ground primary research in actual usage patterns.
- Deliver a scoping research package by end of placement: a prioritised map of user needs and opportunities in the Predictions space, with method, evidence, and recommended product directions clearly documented.
- Present findings to the UX Research team, Product Management, and design stakeholders — with the confidence to defend your methodology and challenge misinterpretations.
Requirements
- Use AI-moderated interview tools (Outset.ai, Remesh, or equivalent) to run structured discovery at a scale not possible with manual moderation alone.
- Currently enrolled in or recently completed an MSc or equivalent postgraduate programme in Psychology, Cognitive Science, Behavioural Science, Human-Computer Interaction, Information Science, Sociology, Applied Data Science, or a closely related field.
- experience running qualitative interviews and designing or analysing quantitative surveys, with the ability to speak to both with methodological honesty. Hands-on
- experience using AI tools in a research or analytical context — actual workflow integration such as LLM-assisted analysis or automated synthesis, rather than basic note-taking use.
- Able to work at pace: optimised workflows, a rapid learning curve, and a willingness to iterate on analytical approach without waiting for the perfect research design.
- Applies AI tools critically: aware of failure modes such as hallucination, over-compression, and confirmation bias in prompt design, and builds validation steps into every AI-assisted workflow.
- Experience with AI-moderated research platforms (Outset.ai, Remesh, Discuss.io, or equivalent). •
- Experience with qualitative analysis tools (Dovetail, NVivo, Atlas.ti, or equivalent).
- Familiarity with Python or R for survey analysis or quantitative data pipelines.
- Interest in or familiarity with sports, gaming, or betting products. Prior internship
- experience in UX research, market research, or behavioural science.
Benefits
- Everything we do at Super is rooted in doing what is right: for customers, for each other, and for our long-term vision.
Additional details
- We are on a mission to pioneer the world’s next era of play.
- As we grow across Europe and Latin America, we’re building The Playstack - the technology powering the next generation of sports, gaming, and fan experiences.
- Join us, and help make it the most widely used platform in the world! From operations, to marketing, to product, we are looking for talented people who will shape how millions of customers play, watch, and connect every day.
- Predictions is one of the most interesting, least understood spaces in sports betting.
- Users form probabilistic beliefs, weigh uncertainty, and act on partial information — yet comparatively little is known empirically about how they engage with prediction features: what they expect, what they trust, what frustrates them, and what makes a prediction feel meaningful rather than noise.
- As a Research Intern on the Predictions team at Super, you will lead scoping research for a product space where the questions are not yet well-formed, user needs are not yet mapped, and design direction has not yet been set.
- Your findings will directly influence how the product is built.
- Triangulate qual depth and quant breadth into a coherent user needs framework for the Predictions space.
- Synthesise across methods into a living insight framework: a structured artefact the product team can interrogate, not a one-off report. Delivery
- Comfortable operating in ambiguous, undefined problem spaces where the brief is "find out what matters" rather than "test this assumption" — and able to generate your own hypotheses and structure your own enquiry.