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.

What the role involves

Discovery & Problem Scoping

• 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.
• Use AI-moderated interview tools (Outset.ai, Remesh, or equivalent) to run structured discovery at a scale not possible with manual moderation alone.
• Design and field a quantitative survey to test qualitatively generated hypotheses, segment the user base on prediction-relevant attitudes and behaviours, and size opportunities.
• Triangulate qual depth and quant breadth into a coherent user needs framework for the Predictions space.

Analysis & Synthesis

• 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.
• Synthesise across methods into a living insight framework: a structured artefact the product team can interrogate, not a one-off report.

Delivery

• 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.

What we are looking for

• 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.
• Genuine mixed-methods capability: 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.
• 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.
• 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.
• Produces outputs that are research packages a product team can build from, not academic exercises.
• Fluent in English (working language of the research function).

Nice to have

• 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.
• Multi-lingual capability relevant to Super's markets (Romanian, Polish, Spanish, Croatian, Dutch, Portuguese, or Italian).