Interview scorecard template

Anti-Money Laundering (AML) Data Scientist interview scorecard

Evaluate Anti-Money Laundering (AML) Data Scientist candidates across 4 weighted areas: technical proficiency, systems and trade-offs, evidence and rigour, and collaboration and communication. Technical proficiency leads at 35%, so probe modelling depth on heavily imbalanced data, plus command of typologies and the sanctions and reporting rules the models serve. Use the rubric to compare role-specific evidence consistently.

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software dataamlcompliancedata sciencefinancial crime
TL;DR
For technical proficiency, look for evidence the candidate strong on imbalanced-data modelling and fluent in the typologies and reporting rules the models exist to serve. For systems and trade-offs, look for evidence the candidate reasons explicitly about detection versus investigator capacity, and names the threshold trade-off they chose. Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.
Complete evaluation framework

What to assess and how to score it

Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.

01
Evaluation factor

Technical proficiency

35% weight

Probe modelling depth on heavily imbalanced data, plus command of typologies and the sanctions and reporting rules the models serve.

Evidence to listen for

  • Command of the languages, frameworks, and data tools the role actually uses
  • Understands correctness, performance, and failure modes, not just syntax
  • Has opinions on testing and can justify them
  • Reads and reasons about code they did not write

Five-point scoring guide

1
Poor

Cannot work independently; fundamentals are missing.

2
Needs Improvement

Weak fundamentals; output needs heavy review.

3
Satisfactory

Competent for the role; needs guidance on complex or unfamiliar work.

4
Very Good

Strong practitioner; handles hard problems with little guidance.

5
Excellent

Strong on imbalanced-data modelling and fluent in the typologies and reporting rules the models exist to serve.

02
Evaluation factor

Systems and trade-offs

25% weight

Test how they balance detection against alert volume, given every false positive costs an investigator's day.

Evidence to listen for

  • Reasons about scale, latency, cost, and failure before writing code
  • Names the trade-off they chose and what they gave up
  • Understands the data lifecycle end to end
  • Anticipates what breaks at ten times the volume

Five-point scoring guide

1
Poor

No thinking beyond the immediate task; no awareness of scale or failure.

2
Needs Improvement

Limited architectural awareness; struggles with design decisions.

3
Satisfactory

Works within an existing design; makes sound local decisions.

4
Very Good

Designs for scale and maintainability; articulates trade-offs clearly.

5
Excellent

Reasons explicitly about detection versus investigator capacity, and names the threshold trade-off they chose.

03
Evaluation factor

Evidence and rigour

25% weight

Check how they validate models when true labels are late, rare, and partly unknowable.

Evidence to listen for

  • Validates results rather than trusting output
  • Knows how their work is measured and what a bad result looks like
  • Can describe a time their own analysis or model was wrong and how they caught it
  • Careful about data quality, leakage, and silent failure

Five-point scoring guide

1
Poor

Ships unvalidated work; no notion of how correctness is checked.

2
Needs Improvement

Validates superficially; misses obvious quality or leakage issues.

3
Satisfactory

Reasonable checks in place; rigour drops under time pressure.

4
Very Good

Validates thoroughly; can name a real error they caught in their own work.

5
Excellent

Validates rigorously despite late and incomplete labels, and can describe a model failure they caught themselves.

04
Evaluation factor

Collaboration and communication

15% weight

Assess how they explain and defend a model to investigators, MLROs, and regulators who need to justify decisions.

Evidence to listen for

  • Explains technical work to non-technical stakeholders
  • Gives and takes code or peer review constructively
  • Documents enough that the work survives their absence
  • Aligns with team process rather than working around it

Five-point scoring guide

1
Poor

Cannot work in a team; resistant to feedback.

2
Needs Improvement

Communication issues create rework; lone-wolf tendencies.

3
Satisfactory

Adequate team member; documentation and review participation are light.

4
Very Good

Communicates well; reliable reviewer and collaborator.

5
Excellent

Explains and defends models to investigators and regulators in terms that support a documented decision.

Evidence-led prompts

Interview questions for a Anti-Money Laundering (AML) Data Scientist

Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.

  1. 01

    How would you describe the job of a data scientist inside an AML programme, as opposed to general analytics?

  2. 02

    What is your understanding of KYC and EDD requirements, and how do they show up in the data you model?

  3. 03

    How familiar are you with FinCEN rules, the FATF recommendations, and the Basel AML Index, and where have they constrained your work?

  4. 04

    Explain what a SAR is, and how suspicious activity you surfaced in analysis actually reached a filing.

  5. 05

    Which machine learning algorithms have you used to identify suspicious activity, and how did you handle the class imbalance?

See the complete Anti-Money Laundering (AML) Data Scientist question set
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