Building the Future of Open Finance
Payward - the parent company behind Kraken, NinjaTrader, Breakout, xStocks, Payward Services and CF Benchmarks - has spent the last 15 years building one of the most modern and globally accessible financial infrastructure platforms in the industry, built to advance an open, global financial system.
The team
Founded in 2011, Kraken is one of the world's longest-standing crypto platforms, trusted by over 10 million individuals and institutions across the globe. It offers spot trading, margin, futures, staking, and OTC services, with products built for both individual investors and institutional clients.
We're looking for a Sr. Data Analyst, Compliance to join our team remotely! The primary goal of this role is to elevate decision-making by uncovering emerging trends and root causes, and by driving insights and actions that provide full visibility into business performance and operational efficiency.
Beyond technical expertise, success in this role depends on being a true business partner who combines data-driven insights with strategic thinking to deliver actionable recommendations, guide decision-making, and drive measurable impact across the organization.
In this role, you'll own end-to-end data management, from building and maintaining datasets and pipelines to designing dashboards and delivering insights for the Compliance team. A core part of this role is owning the logic and rule-setting behind transaction monitoring across deposits and withdrawals. You'll also contribute to experimentation infrastructure, laying the groundwork for future ML and intelligence initiatives.
The opportunity
Own the logic and rule-setting behind transaction monitoring across deposits and withdrawals, partnering closely with Compliance to study patterns and refine detection logic
Translate compliance requirements into production detection logic, preserving correctness across alert eligibility, thresholds, customer segmentation, date windows, and deduplication
Develop and maintain Python and SQL pipelines for transaction data preparation, feature engineering, and detection workflows
Evaluate detection rules and analytical models across coverage, alert quality, threshold sensitivity, and the impact of proposed changes
Design reproducible evaluations on historical data that account for limited labels, class