We’re looking for a Risk Data Analyst to join Integrated Risk Management team. This is a second-line-of-defense role at the intersection of data, banking operations, and risk. You’ll dig into the bank’s core processes, payment infrastructure, and customer-behavior data to catch problems before they turn into real financial losses — using AI and agentic coding to monitor far more than any analyst could by hand.

Challenges that await you:

• Dig deep into the bank’s key processes — understand how data is generated and structured, and what insight can be extracted from it
• Build predictive models of expected process and customer behavior
• Set up broad, scalable monitoring across as many processes as possible, using agentic coding as the default (not the exception) to multiply your reach
• Spot early signals — deviations between actual and expected behavior — that hint at emerging problems
• Investigate anomalies: form hypotheses, validate or disprove them, and trace issues back to their root cause
• Bring concrete findings to process owners and senior stakeholders, and drive them through to resolution

What makes you a great fit:

• Agentic-coding analytics is your primary way of working (essential) — you multiply your reach with AI tools rather than hand-writing everything
• You can read and critically verify agent-generated SQL and Python — follow the logic, reconcile it against the data, and catch errors. Heavy hands-on coding is not required, but you can’t be fooled by plausible-looking output
• Strong analytical judgment — you turn messy, ambiguous data into clear conclusions, and you instinctively question results, including those of your own tools
• Solid working understanding of how a retail bank operates — products (cards, loans, deposits), payment infrastructure, the lending and collection lifecycle, and core operational processes. You need this to know what “normal” looks like
• Anomaly-detection and early-warning mindset — comfortable defining expected behavior and monitoring for deviations at scale
• Statistical and modeling fundamentals (regression, anomaly detection, basic time-series) — enough to direct and sanity-check models
• Autonomy — you take an area end-to-end without constant direction

Nice to have:

• Experience with BI and monitoring tools (Tableau, Looker, Metabase, Power BI, Streamlit)
• Cross-industry breadth — you’ve solved analytical problems in more than one domain
• Risk or banking-domain experience is welcome but not required — we care more about analytical strength and curiosity than years in a risk function