This is one of the most technically demanding individual contributor roles in Superbet's UX function. As Principal Quantitative Researcher, you are the organisation's foremost expert in measuring, modelling, and predicting user behaviour at scale. You don't manage people- you lead through influence, methodology, and the quality of evidence you put in front of key decision-makers.

You will own the quantitative research agenda across Superbet's core product surfaces: sports, gaming onboarding, international growth and loyalty. Your work directly informs millions of user interactions daily. You will set the methodological standards for quant research across the team, mentor peers, and partner with Data Science and Analytics to build a shared measurement culture.

This role is ideal for a researcher who has mastered the craft of quantitative UX and wants to wield it at scale.

What You Will Be Doing

Quantitative Research Strategy & Execution

• Design, own, and execute the quantitative UX research roadmap for Superbet's flagship product areas, spanning attitudinal measurement, large-scale behavioural analysis, and longitudinal tracking.
• Lead the design and analysis of large-scale surveys (n=1,000–50,000+) with psychometrically rigorous instruments: validated scales, conjoint/MaxDiff, SUPR-Q, SUS, custom NPS derivatives, and discrete choice modelling.
• Conduct log-data and behavioural analytics research: funnel analysis, session replay interpretation, A/B and multivariate test design and analysis, event-based cohort studies, and retention modelling.
• Build and own UX measurement frameworks - defining what 'good' looks like quantitatively for acquisition, activation, engagement, and loyalty flows - and track these metrics over time.
• Champion mixed-methods integration: design studies where quant scales what qual discovers, and vice versa — producing evidence packages that are richer than either discipline alone.

AI-Augmented Quantitative Methods

• Pioneer the use of AI and ML techniques within quantitative research: LLM-assisted open-text analysis, NLP-based sentiment classification at scale, clustering algorithms for user segmentation, and predictive modelling of UX outcomes.
• Build and maintain automated survey analysis pipelines using Python/R/SQL that produce repeatable, auditable outputs and reduce manual processing time by order-of-magnitude.
• Evaluate and deploy emerging quant research tooling - AI-powered survey platforms, synthetic respondent validation tools, automated conjoint analysis pipelines - with a critical eye on validity and bias.
• Develop documentation and training materials that enable the broader research team to apply AI-assisted quantitative techniques responsibly and confidently.

Measurement Infrastructure & Standards

• Own Superbet's UX metrics framework and in product survey tool: define the canonical set of product experience KPIs, establish baseline benchmarks, and publish regular tracker reports consumed by product, design, and leadership.
• Partner with Data Science, Business Intelligence, and Product Analytics to align on shared definitions, event taxonomies, and data pipelines - bridging the gap between user experience metrics and business metrics.
• Build and maintain a quantitative research repository: reusable instruments, validated scales, benchmark datasets, and analysis templates that accelerate every future study.
• Define and enforce quantitative research quality standards across the team - reviewing study designs, statistical approaches, and reported effect sizes for rigour and interpretive accuracy.

Influence Without Authority

• Serve as the go-to quantitative authority for the entire product organisation: product managers, designers, engineers, and commercial leads all consult you before designing measurement approaches.
• Translate complex statistical outputs into clear, business-relevant narratives - you never hide behind p-values; you make numbers tell a story that drives a decision.
• Proactively identify measurement gaps and blank spots in the product organisation's understanding of user behaviour, and design studies to fill them before problems surface.
• Mentor 2–4 mid-level quantitative and mixed-methods researchers, elevating the statistical literacy of the broader research and product team through structured coaching and informal pairing.

What We Are Looking For

Essential Experience

• 8+ years of UX research experience with a deep specialisation in quantitative methods - at least 5 years in a product or technology company operating at significant scale.
• Expert-level proficiency in survey methodology: instrument design, sampling theory, quota setting, weighting, and psychometric validation.
• Strong statistical competency: regression (linear, logistic, multinomial), factor analysis, cluster analysis, conjoint/MaxDiff, significance testing, Bayesian inference, and effect size interpretation.
• Hands-on proficiency in at least one analytical language: Python (pandas, scipy, statsmodels, scikit-learn) or R (tidyverse, lavaan, survey package).
• Direct experience working with large behavioural datasets - clickstream, event logs, session data - and the analytics platforms that house them (Amplitude, Mixpanel, BigQuery, Looker, or similar).
• Demonstrated track record of quant research that measurably influenced product strategy, feature prioritisation, or go-to-market decisions.
• Some experience with A/B testing methodology: experimental design, power calculations, sequential testing, interaction effects, and guardrail metrics.

Skills & Mindset

• Statistical rigour without paralysis: You know when 'good enough' quant evidence is sufficient and when you need to go deeper. You never sacrifice rigour for speed, but you don't over-engineer either.
• Data storytelling: Your outputs are not SPSS outputs. They are narratives backed by numbers, calibrated for the audience.
• Proactive measurement architect: You don't wait to be asked. You identify what the organisation doesn't yet know how to measure and build the infrastructure to measure it.
• AI-curious practitioner: You have already incorporated AI tools into your quant workflow and can speak critically about their limitations as well as their power.
• Cross-disciplinary collaborator: You work fluently alongside qual researchers, data scientists, and product analysts - not as competitors but as complementary perspectives.

Nice to Have

• MSc or PhD in Statistics, Quantitative Psychology, Cognitive Science, HCI, Econometrics, or a related quantitative discipline.
• Experience in online gambling, fintech, or other regulated consumer industries where trust, risk perception, and behavioural economics are central to the user experience.
• Familiarity with causal inference methods: difference-in-differences, instrumental variables, propensity score matching.
• Experience with experience sampling / ESM methods or real-time behavioural tracking.
• Published or conference-presented research in a relevant academic or practitioner venue.

Why This Role at Superbet

Quantitative UX research in sports is uniquely challenging: users are making fast decisions under uncertainty, emotion is high, and every design choice has a measurable downstream impact on behaviour. You won't be measuring whether users can find a button - you'll be measuring how design influences risk-taking, trust, enjoyment, and loyalty at scale. You will advocate for what are the right bounds of game playing where a customer is active and healthy. 

• Full access to rich behavioural datasets that most researchers only dream about.
• A product team that is genuinely data-hungry and will act on your findings.
• The mandate to build the quant infrastructure from the ground up.
• Collaborative peers in Data Science and Analytics who respect and value UX evidence.
• Competitive compensation reflective of a Principal IC level with equity participation.