IBM Data Analyst Case Interview — Client Analytics in a Hybrid Cloud Context

What this covers and why it is IBM-specific: This case mirrors real IBM interview patterns: a client-centric scenario, Enterprise Design Thinking (Hills, Sponsor Users, Playbacks), hands-on analytical reasoning with SQL/Python, and a short insights playback. It emphasizes consulting-style problem framing, data governance, and communication, consistent with IBM's culture and interview style. Case prompt (candidate-facing): You are a Data Analyst supporting an IBM Consulting squad on a global retail bank modernizing analytics on a hybrid cloud (Red Hat OpenShift + on‑prem Db2). The bank wants to reduce false-positive fraud alerts while maintaining detection rates and improving call-center efficiency. You will explore anonymized data, define KPIs, propose a lightweight dashboard, and deliver a 10-minute playback of insights and next best actions. Provided artifacts (simulated): - transactions.csv: tx_id, cust_id, ts, amount, mcc, ch_type, device_id, branch_id, is_fraud_flag, alert_id - customers.csv: cust_id, age_band, tenure_months, kyc_risk_tier, region, preferred_channel - alerts.csv: alert_id, ts_alert, risk_score, outcome (cleared/escalated), analyst_minutes_spent - branches.csv: branch_id, region, open_hours_category Assume realistic data issues: missing values, time-zone drift, duplicate alerts, skewed class labels. Interview flow (typical at IBM): 1) Context & alignment (5 min): Clarify business goal; state assumptions and constraints. Use Enterprise Design Thinking language: define a Hill (e.g., reduce false positives by X% without lowering detection), identify Sponsor Users, and success metrics. 2) Exploratory analysis reasoning (10 min): Describe checks you would run (nulls, deduping, outliers, seasonality, leakage). Prioritize questions that de-risk decisions for the client. 3) SQL/Python whiteboard (15–20 min): Write or talk through queries/snippets to: - Compute false-positive rate by day and branch: false_positive = alerts cleared with no fraud on associated tx. - Join transactions to alerts; handle one-to-many alert-to-tx cases. - Window function: rolling 7-day false-positive trend by region. - Feature sketch: derive hour_of_day, weekend_flag, high_risk_mcc, customer_tenure_bucket. 4) KPI design & visualization (10–15 min): Propose a Cognos Analytics (or equivalent) dashboard with: - Executive view: overall false-positive rate, fraud detection rate, analyst minutes per cleared alert, estimated savings. - Operations view: branch/region trends, heat map of alert productivity, drill to analyst workload. - Risk view: precision/recall proxy from outcomes, threshold tuning what-if. Include how you would run "Playback 0/1/2" to validate with stakeholders. 5) Governance & ethics (5 min): Discuss handling PII, data minimization, lineage, and bias checks; reference IBM practices (e.g., watsonx.governance concepts) and controls for regulated workloads. 6) 10-minute playback (final): Deliver a concise narrative: the Hill, what the data says, recommended next best actions, risks, and pilot plan. What good looks like (rubric aligned to IBM expectations): - Client-centric framing and Enterprise Design Thinking: States a clear Hill, identifies sponsor users, proposes playbacks. - Technical depth (SQL/Python): Correct joins, robust aggregation, window functions, edge-case handling; can translate to Db2 or cloud data warehouse. - Analytical rigor: Sensible KPIs, awareness of class imbalance and leakage; quantifies trade-offs (e.g., precision vs operational cost). - Communication: Clear, structured playback; visual choices tied to decisions; concise stakeholder-ready articulation. - Governance and risk: Mentions privacy, access controls, auditability, and fairness considerations. Sample probing questions (interviewer may use): - If data resides on-prem Db2 and needs to be joined with cloud logs on OpenShift, how would you minimize data movement? - How would you test whether lowering the risk threshold truly reduces analyst minutes without increasing missed fraud? - What metric would you present to a COO vs a Fraud Ops lead, and why? - How do you prevent target leakage when deriving features from alerts and outcomes? Logistics and norms (reflecting IBM style): - Tools: Whiteboarding or shared doc; pseudo-SQL/Python acceptable. Visual sketches preferred over polished charts. - Collaboration: Interviewers may play product owner and risk lead; expect clarifying questions and iterative playbacks. - Timeboxing: Expect gentle facilitation to keep segments on track. Expected deliverables by end of case: - Prioritized questions/assumptions, a working KPI set, at least two core queries or pseudo-code segments, a dashboard wireframe, and a brief pilot/measurement plan. Notes for the interviewer running this template: - Provide 1–2 small data anomalies for the candidate to catch (e.g., duplicated alert_ids across days). - Nudge toward quantifying impact (e.g., analyst minutes saved per 1% improvement in false-positive rate). - Score 1–5 across the five rubric dimensions; a strong pass demonstrates balanced client empathy and technical correctness, plus clear playbacks.