Credit Analyst
Job Description: Analyst, Credit Strategy and Underwriting
Team: Risk Operations — Credit Strategy and Analytics
Level: 6 years of relevant experience
Type: Individual contributor, analytical mandate; not a queue-based or SLA-driven role (2
positions)
Coverage: Core business hours
Role Summary
The Analyst, Credit Strategy and Underwriting supports the design, testing, and monitoring credit
strategies across its lending and credit products. The role turns application, bureau, and portfolio data
into analysis the credit strategy team can act on: how underwriting rules and limits are performing,
where the portfolio is drifting, and what a proposed policy change would do to approvals, losses, and
revenue before it goes live.
Most of the work is defined — a recurring report, a requested cut of the data, a back-test of an existing
rule. The value of the role sits in the rest: a result that looks too good, a segment moving against the
portfolio, a shift in performance that could be real borrower behaviour or could be a data artefact. The
analyst is expected to notice these, test them, and say plainly what the data does and does not support.
These are the cases the role exists to get right.
Credit policy, risk appetite, and strategy approvals. The analyst prepares the analysis and the
recommendation and makes the decision.
Key Responsibilities
Credit and Underwriting Analysis
● Analyse application, bureau, and performance data to assess how underwriting rules, score cut-
offs, and credit limits are performing against expectation.
● Size proposed credit policy and limit changes — approval rate, expected loss, exposure, and
revenue impact — before they go forward for approval.
● Review declined and marginal populations to identify where the current strategy is too tight or too
loose.
● Provide the evidence base for periodic credit policy reviews: what is working, what is not, and
what should change.
Strategy Testing and Monitoring
● Design and read out champion/challenger and A/B tests on credit strategies, with sample size,
control group, and success criteria agreed before launch.
● Monitor tests in flight and flag early when results breach agreed guardrails.
● Track vintage, roll-rate, and early-delinquency performance on new strategies against the
expected curve.
● Document test design, results, and recommendations so that decisions can be reconstructed and
audited later.
Portfolio and Performance Reporting
● Build and maintain recurring portfolio reporting: originations, utilisation, delinquency by bucket,
losses, and key risk indicators.
● Reconcile reporting to source data and to finance figures, and explain variances rather than only
reporting them.
● Automate recurring reports and remove manual spreadsheet steps wherever practical.
● Turn results into short written readouts that lead with the conclusion and the recommended
action.
Analytical Rigour and Data Quality
● Test any result that looks unusual before reporting it — check for data issues, definition changes,
or population shifts.
● Separate correlation from cause, and state the confidence behind each finding.
● Raise data quality gaps and inconsistent definitions with the data owner, with a proposed fix.
● Write SQL that is readable, commented, and reproducible by another analyst.
Experience & Qualifications
Level and Tenure
● 6+ years in credit risk, credit strategy, underwriting analytics, or a comparable quantitative role in
financial services.
● Degree in a quantitative discipline (statistics, economics, mathematics, finance, engineering) or
equivalent practical experience.
Required Skills and Experience
● Practical SQL: able to independently write joins, aggregations, and window functions against
large tables.
● Advanced Excel: pivot tables, lookups, and scenario models that another analyst can follow and
audit.
● Working knowledge of consumer credit concepts — bureau scores, delinquency buckets, vintage
analysis, roll rates, and the approval/loss trade-off.
● Analytical judgement: able to explain how a conclusion was reached, what would change it, and
where the data is weak. Assessed at interview through a case exercise.
● Clear written communication — able to turn analysis into a short, specific recommendation for a
non-technical reader.
Preferred
● Python or R for data analysis.
● Experience with consumer lending products such as credit cards, lines of credit, instalment loans,
or credit-builder products.
● Exposure to Canadian credit bureau data.
● Experience with a BI or dashboarding tool.
● Hands-on experience running champion/challenger tests.
What Success Looks Like
● Accuracy — analysis reconciles to source and holds up under review.
● Decision usefulness — recommendations are specific, sized, and acted on; readouts lead with
the answer.
● Test discipline — tests launch with agreed design and success criteria, and read out on
schedule.
● Early warning — adverse portfolio trends and data issues are raised before they show up in
losses.
● Efficiency — recurring reporting becomes progressively more automated quarter over quarter.