Product Manager, AI & Planning Science

Company Description

Vuori is re-defining what athletic apparel looks like: built to move and sweat in but designed with a casual aesthetic to transition into everyday life. We draw inspiration from an active coastal California lifestyle; an integration of fitness, creative expression and life. Our high energy fast paced retail environment is reflected in the clothes we make. We aim to inspire others to take on all aspects of their lives with clarity, enthusiasm and purpose…while having a lot of fun along the way. We are proud to be an outlet for opportunity and for personal growth and success.

Job 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.

Manage launches end to end: rollout plans, user enablement, stakeholder communication, and post-launch monitoring to confirm the work is performing as intended.

When the portfolio calls for it, extend the same delivery discipline into agentic AI and generative AI projects, applying consistent product rigor regardless of the underlying technology.

Partner and measure

Build strong working relationships with merchandising, planning, supply chain, finance, marketing, and CRM teams to understand their workflows, data environment, and the decisions where better models would make a meaningful difference.

Support customer and consumer analytics initiatives — including customer lifetime value modeling, segmentation, churn prediction, propensity scoring, and personalization — ensuring outputs are actionable for marketing and e-commerce teams.

Work with data scientists and analytics teams to track model performance in production — accuracy, drift, coverage, and downstream business impact — across both planning and consumer-facing products.

Collect and synthesize stakeholder feedback, model performance data, and user behavior to maintain a continuous improvement backlog.

Advocate for the end users of planning science and consumer analytics products — ensuring model outputs are interpretable, trustworthy, and fit for the decisions being made.

Provide regular progress updates and outcome summaries to senior leadership, grounded in model performance metrics and business results.

Qualifications

Who you are:

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.

Preferred Qualifications

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 m