Day Shift Remote Data Labelling Jobs in New York

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Locations Supported

• London, UK (primary)

• Remote (Europe)

Relocation available: Case by case

Work pattern:

• Remote across Europe, with hybrid working encouraged if you're near a Moonbase (around 2 to 3 days per week in the London office).

About the Opportunity

In this role, you'll help shape MoonPay's product and business decisions through data, combining deep analysis, experimentation, and strong stakeholder partnership to drive real impact.

You'll sit within our centralised Product Data team and work closely with Product and Engineering, owning analytics across the full product lifecycle, from defining problems and success metrics through to launching, measuring, and optimising products at scale.

Your work will directly influence product strategy, customer experience, and business health, helping teams make better, faster decisions through trusted insights and clear narratives. You'll also raise the leverage of the whole team by building reusable, AI-assisted analytics skills that let the wider org self-serve reliable answers.

This is a great opportunity for someone who enjoys turning complex data into clear direction, influencing decisions, and helping shape the future of high-impact financial and crypto products.

What You Will Do

• Partner with product and business teams to run rigorous analysis (root cause, opportunity sizing, funnel drop-off) and turn it into clear, prioritised recommendations, moving across problem areas as priorities shift.

• Define, document, and maintain clear metric definitions and business context, so stakeholders can understand the performance of their area and AI tools can interpret our metrics correctly (e.g. Transaction Success Rate, Fraud Rate).

• Set up tracking and monitoring for new launches and changes, and quantify and measure their impact on KPIs.

• Build and maintain a reporting layer, with automated alerting and root cause analysis that flags when KPIs move unexpectedly and why.

• Design, run, and interpret experiments, from A/B tests to quasi-experimental methods like difference-in-differences when randomisation isn't possible.

• Model robust, trusted data in our dbt and BigQuery layer, safeguarding its quality through testing, documentation, and certification in collaboration with engineering.

• Create reusable, AI-assisted analytics skills that scale the team's impact and help Product and Engineering self-serve.

• Communicate complex concepts and findings to technical and non-technical audiences across different teams, while ensuring clarity and understanding to drive critical business decisions.

About You

Must-have experience and skills

• 5+ years of hands-on experience as a data analyst or data scientist, preferably in a product-focused role.

• Advanced SQL and data visualisation are second nature to you.

• Hands-on experience building data models in a modern cloud warehouse and transformation framework (e.g. dbt with BigQuery, Snowflake), with a strong instinct for data quality.

• A solid grasp of statistics and probability, including experiment design and interpretation (A/B and quasi-experimental methods such as difference-in-differences).

• A working understanding of how a payments or fintech business operates, including fraud, chargebacks, KYC/AML, and payment success rates.

• Comfort using AI-assisted and LLM tools to accelerate analysis, and curiosity about building reusable skills that scale your impact.

• Exceptional communication and stakeholder skills. You can distill complex findings for any audience up to C-level, and lead projects independently.

Nice-to-have experience

• Familiarity with our stack (dbt, BigQuery, Looker, Python) and AI tooling such as Claude Code.

• Hands-on financial crime, payments, or fraud analytics experience.

• Experience working in start-ups or scale-ups.

Bonus Points

• A crypto-native perspective and experience with on-chain and blockchain data (e.g. Dune, Flipside, Chainalysis).

• Experience making data self-serve for non-analysts (e.g. semantic layers, metric stores).

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.