Sell GPUs to a retail CEO
Overview
This question evaluates a candidate's ability to build a business and technical case for deploying GPUs across enterprise analytics and AI workloads, testing competencies in ROI/TCO modeling, workload discovery, pilot planning, stakeholder alignment, risk identification, and crafting data‑backed rebuttals.
NVIDIA
Oct 13, 2025, 9:49 PM
Data Scientist
HR Screen
Behavioral & Leadership
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Scenario: Pitching NVIDIA GPUs to Walmart’s CEO
You are a data scientist preparing an executive-ready proposal to justify adopting NVIDIA GPUs across Walmart’s priority analytics and AI workloads.
Deliverables:
- Discovery: Identify high‑impact, GPU‑amenable workloads (e.g., demand forecasting, route optimization, computer vision, LLM assistants). For each, note data sources, current pain points, latency/throughput needs, and value drivers.
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ROI and TCO: Build a quick, defensible TCO model comparing GPU vs CPU, including:
- CapEx and OpEx (hardware, power/cooling, space, software/ops)
- Realistic utilization assumptions and consolidation impact
- Latency‑to‑revenue pathways (how faster/more accurate models create dollars)
- A small numeric example to illustrate payback
- Pilot Plan: Propose a phased 90‑day pilot with use‑case scope, architecture, success metrics, and acceptance thresholds for scale‑up.
- Stakeholders & Risks: Identify executive sponsors and operational owners; enumerate risks (edge deployment, vendor lock‑in, data security/privacy, ESG) and mitigations.
- Objections & Rebuttals: Prepare data‑backed rebuttals to three common objections (e.g., GPUs are too expensive; we lack skills; cloud is enough).
- Clear Ask & Timeline: End with a crisp executive ask (budget, access, stakeholders, scope) and a 90‑day timeline with milestones.
Make explicit, minimal assumptions where needed, and keep numbers illustrative but defensible.
Solution
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