Senior Optimization Data Analyst at Adyen · LumiJobs

Responsibilities

Domain-Led Merchant Engagement: Lead PO's most complex merchant engagements in the risk, fraud, and ML optimization domain. This includes designing and interpreting sophisticated analyses, advising merchants on how to safely operationalize ML-driven decisioning systems, identifying performance gaps that require domain-level expertise to diagnose, and driving strategic improvements in fraud strategy, risk rule configuration, authentication flows, and authorization performance across Adyen's largest customers.
Risk Analysis and Insight Generation: Work directly with large-scale risk model data, including ML output and labeled risk data, to analyze model behavior, detect performance patterns, and generate insights across merchant segments, traffic types, and risk profiles. In close collaboration with Adyen's product teams, contribute to building an analytical layer that connects model behavior to merchant strategy, improving explainability and enabling more targeted optimization. This work requires the ability to go beyond standard analytics and work with raw, complex data to surface what is not yet visible.
Product Collaboration and Portfolio Intelligence: Work closely with Adyen's risk product teams to contribute structured, data-backed insights based on patterns observed across the merchant portfolio. Identify where risk model configurations produce inconsistent outcomes across segments, where merchant strategies diverge from model behavior, and where analytical findings can inform product priorities, as well as ensure merchants are equipped to configure, trust, and act on model-driven recommendations. This contribution operates at the portfolio level, going beyond the level of individual merchant escalations, and is grounded in data, rather than isolated merchant observations.
Analytical Excellence and Methodology Ownership: Drive the quality and rigor of PO's analytical work in the risk and fraud domain. Design and execute advanced data analyses, A/B tests, and statistical investigations that set the methodological standard for the broader team. Own the development of reusable analytical frameworks, experiment designs, and best practices that other ODAs and OMs can apply independently. Operate with a high degree of autonomy: this role is expected to self-direct from problem scoping through to insight and recommendation without requiring active management to progress.
Automation and Scalable Technical Solutions: Lead or contribute to automation initiatives that expand PO's reach beyond direct merchant engagement. Build scalable data products, tools, and analytical workflows that allow commercial stakeholders, including Account Managers and Sales teams, to access and act on risk and fraud optimization insights without requiring PO involvement for every interaction. Identify where engineering effort creates the highest leverage and execute accordingly.
Commercial Enablement and Knowledge Transfer: Develop playbooks, and reference frameworks that enable PO and commercial stakeholders to engage more confidently on risk and fraud optimization topics. Reduce PO's dependency on individual expertise by making domain knowledge transferable, documented, and accessible. Mentor and provide technical guidance to other analysts, fostering a culture of analytical rigor and continuous learning within the team.
Emerging Optimization Domains: Develop and maintain a forward-looking perspective on where the risk and fraud optimization surface is evolving. This includes the implications of ML adoption curves, the declining value of scenario-based rules, and the emerging authentication and fraud challenges introduced by agentic commerce and agent-initiated payment flows. Translate this perspective into actionable frameworks for merchants and internal teams before these challenges become acute.
What you'll do reflects a role that spans technical depth, merchant impact, and organizational multiplier effect. The expectation is not to do all of these with equal intensity at all times, but to operate across all of them with increasing ownership and impact over time.

Qualification

Expert-level proficiency in PythonProven ability to lead high-impactOwnership mentality

Required

10+ years of relevant experience in data analytics, data science, risk, or fraud optimization, with a demonstrated track record of driving measurable impact in a payments or fintech environment.
Deep expertise in risk and fraud analytics, including hands-on experience working with ML-based decisioning systems and large-scale risk model data. You are comfortable working with raw model output and labeled risk data to generate insights that go beyond standard analytics.
Expert-level proficiency in Python, SQL, and PySpark, with hands-on experience in large-scale data processing, scalable ETL pipeline development, and production-grade analytical systems.
Strong experience with data visualization and self-service analytics tools such as Looker or Tableau, with the ability to design dashboards and tools that non-technical stakeholders can use independently.
Deep understanding of statistical methods including hypothesis testing, experiment design, regression modeling, and the ability to apply these rigorously in a business context where decisions have commercial consequences.
Experience with big data technologies including Spark and Airflow, and version control systems including Git, with a standard of engineering quality that produces reliable, maintainable, and scalable outputs.
Demonstrated ability to work at the boundary of technical and commercial: translating complex analytical findings into merchant-facing recommendations, product feedback, and strategic narratives that influence decisions at the senior level.
Proven ability to lead high-impact, cross-functional initiatives independently, with the organizational credibility and communication skills to align stakeholders across commercial, product, and data functions.
A forward-looking perspective on where payments risk and fraud optimization are heading, including familiarity with how ML adoption is reshaping risk strategy, how authentication frameworks are evolving under regulatory and commercial pressure, and how emerging payment patterns such as agentic commerce introduce new fraud and dispute challenges.
Ownership mentality, intellectual curiosity, and the ability to operate in a fast-paced, cross-functional, global environment where ambiguity is the norm and impact is the measure.
A track record of making the people and teams around you more capable: through mentorship, knowledge transfer, methodology development, and tooling that scales your expertise beyond your own direct output.
Payments or fintech industry experience is required at this level, not a plus.

Senior Optimization Data Analyst

Responsibilities

Qualification

Required

Benefits

Company

Adyen

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