Product Manager, AI & Planning Science
- Compensation
- Not disclosed
- Location
- Carlsbad, CA, us
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Product
- Posted
Skills
Must have
- Bachelor's degree in Business, Computer Science, Statistics, Engineering, or a related field; or equivalent years of experience preferred.
- 8+ years of product management experience, including 3+ years working directly with data science or machine learning teams on products shipped to real users in production environments.
- Demonstrated track record of managing a roadmap and delivering data science or ML features end to end, with measurable business outcomes across planning, operations, or customer-facing domains.
- Strong ability to work alongside data scientists and ML engineers — comfortable discussing modeling approaches, evaluation of metrics, and data requirements at a meaningful level of depth.
- Experience translating business planning, forecasting, customer analytics, or operational needs into clear model requirements and product specifications.
- Familiarity with the full ML lifecycle — data preparation, model development, evaluation, deployment, and monitoring — and the product considerations at each stage.
- Excellent written and verbal communication skills, with the ability to make modeling work legible and credible to business, executive, and technical audiences alike.
- Strong organizational skills and a consistent bias toward action, with comfort navigating the inherent uncertainty of data science delivery.
Nice to have
- Experience in retail, e-commerce, direct-to-consumer, or consumer goods — particularly across planning and customer analytics domains.
- Familiarity with planning science domains such as demand sensing, replenishment optimization, size curve modeling, or markdown optimization.
- Familiarity with customer science domains such as customer lifetime value, segmentation, churn modeling, propensity scoring, or AI-driven personalization.
- Exposure to agentic AI, LLM-powered workflows, or generative AI applications in a business context — either through direct delivery or adjacent product work.
- Comfort working with data directly — SQL, BI tools, or experimentation frameworks — to independently assess model performance and product outcomes.
- Experience with ML platforms or tools such as Azure ML, Databricks, SageMaker, or similar.
- Familiarity with responsible AI principles as they apply to predictive modeling — bias, fairness, explainability, and model governance.
- Demonstrated curiosity about emerging data science and AI techniques, with a history of bringing new capabilities into production alongside technical teams.
Full description
As AI and Planning Science Product Manager, you will be responsible for maintaining the roadmap and driving successful delivery of Vuori's data science and machine learning products. Your primary focus will span two connected domains: planning science capabilities — forecasting, inventory optimization, demand sensing, and the ML models that power smarter merchandising and supply chain decisions — and customer and consumer analytics, including models and data products that deepen understanding of customer behavior, lifetime value, segmentation, and personalization. You will also flex into agentic AI and generative AI initiatives as the portfolio evolves.
This role is a strong fit for a product practitioner who is comfortable working closely with data scientists and ML engineers, translating complex modeling work into products that business teams actually use and trust. You will keep delivery moving, ensure requirements are clear and grounded in real business needs, and stay close to outcomes so the team always knows whether the work is landing.
What you'll get to do:
Maintain and manage the roadmap
- Keep the planning science and consumer AI product roadmap current, sequenced, and visible — balancing near-term delivery with longer-horizon model and platform investments across both planning and customer domains.
- Work with merchandising, supply chain, finance, marketing, e-commerce, and technology partners to surface high-value opportunities for data science and ML, and bring them into the roadmap through a clear prioritization process.
- Facilitate trade-off conversations between business stakeholders and data science teams, helping groups reach decisions grounded in data and realistic about what modeling can deliver.
- Communicate roadmap status, sequencing changes, and delivery progress on a regular cadence so stakeholders are not surprised and plans remain credible.
- Surface dependencies, data readiness gaps, and model risk issues early so teams have time to address them before they become blockers.
Drive successful delivery
- Translate business planning, operational, and customer-facing needs into clear, detailed product requirements, model evaluation criteria, and acceptance standards that data science and engineering teams can build against.
- Work alongside data scientists and ML engineers throughout development — clarifying scope, reviewing model outputs, aligning on evaluation frameworks, and making fast decisions when requirements need to adapt.
- Ensure model outputs are properly integrated into business workflows and decision tools — not just technically functional, but genuinely usable by the planners, marketers, merchants, and operators who depend on them.
- Run agile ceremonies and delivery rituals that keep cross-functional teams — data science, engineering, analytics, and business — aligned and moving forward.