Choose a specific instance where your analysis provided new, decision-relevant insights that were not previously considered, and clearly articulate how you communicated these findings to stakeholders to influence the decision. Emphasize the impact of the change and the collaborative process that led to the decision reversal.
Pro tip: Highlight the importance of building credibility and trust with stakeholders before presenting contradictory evidence; this demonstrates maturity and increases the likelihood of your analysis being acted upon.
Briefly describe the project, the decision that was already in motion, and why it was being pursued. Provide enough background for the interviewer to understand the stakes.
Detail the analysis you conducted, including the data sources, methods, and key findings that challenged the existing decision. Focus on the insights that were new or overlooked.
Describe how you presented your findings to stakeholders, including any challenges you faced and how you addressed them. Emphasize clear, persuasive communication tailored to the audience.
Explain the outcome: how your analysis led to a change or adjustment in the decision. Highlight the stakeholder discussions and the rationale for the change.
Summarize the results of the decision change, including any metrics or business outcomes. Reflect on lessons learned and how this experience has shaped your approach to data-driven decision making.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Trickier than it sounds because they want triage, not just 'I flagged it.' I talked through isolating the bad pipeline, cross-checking against a secondary source, and communicating to stakeholders that the metric was on hold while we investigated.
Use the STAR method to describe a specific instance where you identified unreliable data, focusing on your diagnostic process and the corrective actions you took. Emphasize collaboration with data engineers and the impact of your actions on downstream analysis or business decisions.
Pro tip: Quantify the impact of the unreliable data and your fix—e.g., 'prevented a $X misallocation' or 'improved model accuracy by Y%'—to demonstrate business acumen, which is highly valued at Citi.
Briefly describe the project, the data source, and what the data was supposed to represent, highlighting why reliability was critical.
Explain how you discovered the data was unreliable—e.g., through anomaly detection, validation checks, or unexpected results—and the initial signs that raised suspicion.
Detail your systematic approach to diagnose the root cause, such as tracing data lineage, checking ETL processes, or consulting with data engineers.
Describe the steps you took to address the issue, including cleaning data, implementing validation rules, or collaborating with engineering to fix pipelines.
Explain how you ensured the issue wouldn't happen again and quantify the positive impact of your actions on the project or business.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
My instinct was to talk about asking clarifying questions, which is fine but pretty surface level.
Use a structured discovery process to clarify the request, focusing on the underlying business problem rather than the initial ask. Demonstrate how you translate vague stakeholder needs into actionable data science projects, while managing expectations and aligning with Citi's regulatory and business context.
Pro tip: Frame your approach as 'problem-first, not solution-first' and emphasize that you document assumptions and get stakeholder sign-off to avoid scope creep. In a bank like Citi, also mention the importance of considering data governance and compliance early.
Let the stakeholder explain their request fully without interrupting. Acknowledge their need and thank them for bringing it to you, showing you value their input.
Probe to understand the business problem, desired outcome, and success metrics. Ask 'what problem are we trying to solve?' and 'how would you measure success?' to uncover the real need.
Summarize your understanding back to the stakeholder to ensure alignment. Confirm the problem statement, scope, and any constraints (e.g., data availability, timeline).
Suggest a collaborative next step, such as a follow-up meeting, a small exploratory analysis, or a written problem statement. Outline how you'll prioritize this against existing work.
Document the clarified requirements and share with the stakeholder for final confirmation. Set expectations on next steps and timelines, and loop in other teams if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.