Job Opportunities API

# Marketing Experts — Planning AI Project ## 🏢 About Vetto Vetto is a global platform that connects top-tier professionals to strategic Artificial Intelligence projects around the world. Our mission is to build trust, quality, and long-term value within the AI ecosystem, for both exceptional talent and companies operating at the forefront of technology. ## 📖 About the project We're recruiting marketing experts to review and improve real-world scenarios used to train AI planning assistants in an educational context. The AI model will act as a tutor — structuring marketing problems, mapping decision trees, and teaching data-driven reasoning to students. Your job is to think like a senior marketer: break down campaign or growth challenges, identify alternatives, justify decisions with concrete data, and ensure the reasoning is both rigorous and clear enough to teach. ## 👤 Who can apply Professionals from any marketing discipline — digital, growth, branding, performance, content, product marketing, or CRM. Anyone with hands-on experience diagnosing marketing problems and making data-driven decisions. Final-year undergraduate students in marketing, business, or communications with practical project experience are also welcome to apply. ## 🧩 Selection In this application, you will answer questions following the instructions below. If selected, you will be invited to review real marketing case scenarios as part of the project. For the reasoning case, present a real marketing problem you diagnosed or solved — a campaign underperformance, channel decision, audience targeting challenge, growth bottleneck, brand positioning issue, etc. You may anonymize it. We are not evaluating whether your conclusion was right. We are evaluating how you think. The case is structured in 4 parts: **Part 1 — The Problem:** describe the marketing problem and what data or information you had available at the start. **Part 2 — Your Journey:** describe your reasoning in 3 steps. For each step, explain what you analyzed or decided and what specific metric, data point, or insight drove that decision. **Part 3 — Discarded Alternatives:** for each of the 3 steps, list at least 2 hypotheses you considered but ruled out and explain what concrete data eliminated each one. "It wasn't the case" is not a valid answer. **Part 4 — Conclusion:** describe the final recommendation or solution and how the evidence you gathered led to it. Also highlight 1–2 key insights — the most important findings or turning points in your reasoning: a metric that confirmed your direction, a detail that ruled out a strong alternative, or a non-obvious observation that most people would have missed. **Example:** Part 1 — The Problem A D2C brand was running paid social campaigns on Meta and Google but saw CAC increase 40% over 2 months with no change in budget. Leadership suspected creative fatigue but had no data to confirm it. Part 2 — Your Journey Step 1: Segmented performance by channel — 90% of the CAC increase came from Meta, while Google remained stable, which isolated the problem to a single channel rather than a broad market issue. Step 2: Analyzed Meta campaign data by ad set and creative — CTR on the top 3 creatives had dropped from 3.2% to 0.9% over 6 weeks, confirming creative fatigue on the best-performing assets. Step 3: Tested 4 new creative concepts with a 20% budget split — 2 concepts recovered CTR to 2.8% within 10 days, validating creative refresh as the primary lever. Part 3 — Discarded Alternatives Step 1 — Alternative 1: Audience saturation across all channels / Ruled out by: Google performance held steady — saturation would have affected both channels simultaneously. Step 1 — Alternative 2: Seasonality drop / Ruled out by: industry benchmarks for the period showed flat or growing performance for comparable brands. Step 2 — Alternative 1: Audience targeting issue / Ruled out by: CPM and audien