Data Analyst – Risk & Compliance

Key Responsibilities:

Analyze large-scale transaction datasets using SQL to identify fraud, AML, and compliance risk patterns.
Translate analytical findings into clear business actions (rule tuning, thresholds, workflows, policy inputs).
Work with Compliance, Risk, and Product teams to convert regulatory requirements into data logic and system rules.
Evaluate effectiveness of risk controls using data (alert rates, false positives, precision/recall, conversion impact).
Design, build, and maintain dashboards and reporting that drive decision-making.
Perform deep-dive analyses and investigations to support financial crime initiatives.
Identify gaps and inefficiencies in current processes and recommend data-driven improvements.
Partner with engineering to implement scalable and auditable decisioning systems.

Requirements:

3–6 years of experience in data analytics, risk, fraud, compliance, or financial services.
Strong proficiency in SQL (mandatory) ability to independently extract, join, and analyze large datasets.
Proven ability to connect data insights to business decisions and outcomes.
Experience building dashboards / visualizations (Tableau, Power BI, Looker, or similar).
Strong analytical thinking with a problem-solving, data-to-decision mindset.
Ability to work across technical and non-technical stakeholders.
Experience in fraud detection, AML, or transaction monitoring systems.
Exposure to rule engines, decision systems, or risk platforms.
Familiarity with Python (Pandas, NumPy) for data analysis.
Understanding of experimentation / threshold tuning / performance optimization.

What Success Looks Like:

You independently query and break down complex datasets without relying on others.
You move from data → insight → action quickly and clearly.
You improve risk systems with measurable impact (e.g., reduced false positives, improved detection rates).
You think in terms of signals, patterns, and system behavior, not just reports.
What We’re Really Looking For
You don’t wait for data - you pull it yourself.
You don’t just present numbers -you recommend actions.
You can connect data → risk insight → rule/system change → measurable outcome.
You are comfortable operating across data, business, and system logic.