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Stellantis

Content Value & Incentive Efficiency Analyst

16 days ago by Stellantis
  • Salary negotiable
  • Auburn Hills, MI, US
  • Full-time
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AI summary

This analyst role links product content decisions to incentive spend, building models that quantify how much incentive budget is covering content-value gaps and what spend can be eliminated when content changes are made. You'll work onsite with Stellantis, producing content–incentive recommendations for teams like Brand Operations, Product Planning, Dealer Network, and Brand Finance. Standout perk: high-impact ownership of a core analytical framework connecting content and incentive optimization.

Key skills
Analytical modelingIncentive-to-content substitution modelingConfiguration and trim-level analysisCompetitive benchmarkingMarket intelligence analysisDealer ordering analysisCross-functional stakeholder communicationRecommendation development for business teams
Salary not listed — comparable US automotive strategy/analytics roles in Auburn Hills typically pay around $80k–$120k depending on experience level.
You'll thrive here if you enjoy building rigorous models that translate product-content changes into measurable incentive spend and volume outcomes for multiple commercial stakeholders.
Why apply
  • High-impact incentive efficiency modeling
  • Cross-functional visibility and influence
  • Onsite role in Auburn Hills

The Content Value & Incentive Efficiency Analyst quantifies how much of Stellantis's current incentive spend is structural compensation for a content-value gap — and calculates exactly how much of that spend becomes eliminable when specific content changes are made. The analytical link between Content Optimization and Incentive Optimization.

This role will be a primary user of the analytical framework assessing content application, competitive benchmarking, market intelligence and dealer ordering. They will inform recommendations for Brand Operations, Product Planning, Dealer Network and Brand Finance.

Key Responsibilities

  • Build the incentive-to-content substitution model: estimate, by configuration and trim, how much incentive spend is structurally compensating for a content gap vs. driving incremental volume.
  • Quantify the incentive avoidance opportunity: for each proposed content change, estimate the incentive spend that becomes eliminable at equal volume.
  • Build the round-trip content-incentive analysis with Incentive Optimization: estimate how content improvements in high-volume markets reduce the incentive requirement in the deployment model.
  • Produce the joint Content–Incentive recommendation inputs to support total content optimization strategy.
  • Support the investment reconciliation between Content Optimization and Incentive Optimization.
Reference: 4268_5001200290000·Original posting
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