Data Scientist

• Reframe ambiguous requests around key business decisions and connect econometric work to revenue, cost, or churn levers
• Run A/B tests and design studies such as synthetic controls or staggered rollouts when clean randomization is scarce
• Build reduced-form and simple structural models, adapting established methods to real-world challenges
• Architect robust metrics and metric trees, and write production-quality SQL and Python to shape data at scale
• Translate causal findings and statistical uncertainty into insights that guide non-technical decision-makers
• Use AI tools to accelerate coding and analysis while applying critical judgment and catching errors in theoretical reasoning

Technologies:

• AI
• Architect
• LESS
• Python
• SQL
• Spark

More:

We are Dojo, a payments company whose infrastructure is purpose-built for in-person commerce. In less than four years, we became the UKs largest and most loved acquirer, and more than 150,000 customers across four countries use our services. Data is central to our ambition to empower the experience economy. We give exceptional people autonomy, trust, and ownership, and believe our people are our greatest investment. We have an office-first culture, with employees working from the office four or more days per week, and offices across Europe featuring quiet zones, collaboration spaces, and social hubs. Our values are curiosity, persistence, and customer obsession. We are committed to diversity, equity, and inclusion, and aim to build teams that reflect the businesses we serve. We provide recruitment-process adjustments when needed, and information about our benefits and workplace is available on our careers site.

• Deep command of causal inference and econometric methods, including instrumental variables, regression discontinuity designs, difference-in-differences, and fixed effects
• Strong theoretical foundation and ability to reason from first principles when standard methods do not fit
• Proficiency in statistical programming with Python and SQL for sourcing and shaping data at scale
• Commitment to quantifying uncertainty, stating assumptions, and protecting research integrity
• Ability to explain complex causal findings through clear, compelling narratives for non-technical stakeholders
• Curiosity, persistence, and a customer-first mindset