Product Manager, AI & Planning Science

Vuori•Carlsbad, CA

About The Position

As AI and Planning Science Product Manager, you will maintain the roadmap and drive successful delivery of Vuori’s data science and machine learning products. The role spans planning science capabilities—including forecasting, inventory optimization, demand sensing, and the ML models that support merchandising and supply chain decisions—as well as customer and consumer analytics, including models and data products for customer behavior, lifetime value, segmentation, and personalization. The role may also support agentic AI and generative AI initiatives as the portfolio evolves. You will work closely with data scientists and ML engineers to translate complex modeling work into useful, trusted products for business teams. You will keep delivery moving, ensure requirements reflect real business needs, and monitor outcomes to assess whether products are performing as intended. The work includes roadmap management, requirements definition, delivery coordination, launch management, stakeholder partnership, model-performance measurement, and continuous improvement.

Requirements

Bachelor’s degree in Business, Computer Science, Statistics, Engineering, or a related field, or equivalent years of experience; listed as preferred in the posting.
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 managing a roadmap and delivering data science or ML features end to end with measurable business outcomes across planning, operations, or customer-facing domains.
Ability to work alongside data scientists and ML engineers, including discussing modeling approaches, evaluation metrics, and data requirements in meaningful depth.
Experience translating business planning, forecasting, customer analytics, or operational needs into model requirements and product specifications.
Familiarity with the ML lifecycle, including data preparation, model development, evaluation, deployment, and monitoring, and the product considerations at each stage.
Excellent written and verbal communication skills, including the ability to explain modeling work to business, executive, and technical audiences.
Strong organizational skills, a bias toward action, and comfort navigating uncertainty in data science delivery.

Nice To Haves

Experience in retail, e-commerce, direct-to-consumer, or consumer goods, particularly in planning and customer analytics.
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, through direct delivery or adjacent product work.
Comfort working directly with data using SQL, BI tools, or experimentation frameworks to 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 for predictive modeling, including bias, fairness, explainability, and model governance.
Curiosity about emerging data science and AI techniques, with a history of bringing new capabilities into production alongside technical teams.

Responsibilities

Maintain and manage a current, sequenced, visible roadmap for planning science and consumer AI products, balancing near-term delivery with longer-term model and platform investments.
Partner with merchandising, supply chain, finance, marketing, e-commerce, technology, planning, and CRM stakeholders to identify valuable data science and ML opportunities and prioritize them.
Facilitate trade-off discussions between business stakeholders and data science teams, supporting decisions grounded in data and realistic modeling expectations.
Communicate roadmap status, sequencing changes, and delivery progress regularly; surface dependencies, data-readiness gaps, and model risks early.
Translate business planning, operational, and customer-facing needs into detailed product requirements, model evaluation criteria, and acceptance standards.
Work with data scientists and ML engineers throughout development to clarify scope, review model outputs, align evaluation frameworks, and adapt requirements when needed.
Ensure model outputs are integrated into workflows and decision tools in ways that are usable by planners, marketers, merchants, and operators.
Run agile ceremonies and delivery rituals to keep data science, engineering, analytics, and business teams aligned.
Manage launches end to end, including rollout plans, user enablement, stakeholder communications, and post-launch monitoring.
Apply consistent product delivery practices to agentic AI and generative AI projects when they are part of the portfolio.
Build relationships with business teams to understand workflows, data environments, and decisions that could benefit from improved models.
Support customer and consumer analytics initiatives such as lifetime value modeling, segmentation, churn prediction, propensity scoring, and personalization, ensuring outputs are actionable for marketing and e-commerce teams.
Work with data science and analytics teams to track production model accuracy, drift, coverage, and downstream business impact.
Collect and synthesize stakeholder feedback, model performance data, and user behavior to maintain a continuous improvement backlog.
Advocate for end users by ensuring model outputs are interpretable, trustworthy, and appropriate for the decisions they support.
Provide senior leadership with regular progress updates and outcome summaries grounded in model-performance metrics and business results.