Position summary Lead analytics and data management for a compliance team supporting a cryptocurrency and financial services platform. Own transaction monitoring logic across deposits and withdrawals, develop reliable datasets and pipelines, and turn complex findings into actionable recommendations. This role combines business partnership, statistical analysis, and hands-on ownership of production detection workflows. Your responsibilities Own transaction monitoring rules, partnering with Compliance to analyze patterns and refine detection logic. Translate compliance requirements into production logic covering alert eligibility, thresholds, customer segments, date windows, and deduplication. Build and maintain Python and SQL pipelines for transaction data preparation, feature engineering, and detection workflows. Evaluate detection rules and analytical models for coverage, alert quality, threshold sensitivity, and the impact of proposed changes. Design reproducible historical evaluations that account for limited labels, class imbalance, and changing customer behavior. Manage testing, deployment, scheduling, monitoring, and operational support for detection workflows. Investigate data quality issues, pipeline failures, and unexpected changes in detection results. Execute historical backfills with validation and traceability, maintaining reliable alert delivery and integrations with compliance systems. Develop dashboards and insights that improve visibility into business performance and operational efficiency. Test hypotheses and contribute to experimentation infrastructure supporting future machine learning initiatives. Experience and qualifications 7+ years of experience in data analysis, data science, and data management within fintech, cryptocurrency, financial services, or gambling in a fast-paced, high-growth environment. Experience partnering with compliance, fraud, funding, finance, and customer support teams. Experience analyzing large datasets, engineering features, and applying statistical methods to business problems. Strong Python and SQL skills, including building and supporting production data or analytical systems. Hands-on experience with dbt or similar data modeling tools. Experience developing data pipelines and managing workflow orchestration. Strong software engineering practices, including version control, automated testing, code review, and CI/CD. A solid statistical foundation and proficiency with BI tools such as Hex, Tableau, or Power BI. Ability to investigate production issues independently and explain technical findings and uncertainty to non-technical stakeholders. A degree in an analytical discipline such as analytics, software engineering, statistics, or economics. Fluency in English. Additional strengths Experience with fraud detection, AML, anomaly detection, or other risk applications. Familiarity with Athena, Trino, Kafka, and Kubernetes alongside Airflow, dbt, and S3. Experience analyzing transaction patterns, segmenting customers, and tuning detection thresholds. Experience developing or deploying machine learning models, including experiment tracking and performance monitoring. Ability to use sparse or delayed investigation feedback to evaluate and improve detection. Experience with explainability, auditability, and change control in regulated environments. Compensation and benefits Bonus and equity programs. Wellness allowance. Medical, dental, and vision benefits for U.S. employees. 401(k) for U.S. employees.