Product Manager, AI & Planning Science

Vuori, Inc•Carlsbad, CA

19h•$137,300 - $176,610•Onsite

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 ML models for 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. You 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 products that business teams use and trust. The role involves keeping delivery moving, grounding requirements in business needs, and tracking outcomes to assess whether products are performing effectively.

Requirements

Bachelor's degree in Business, Computer Science, Statistics, Engineering, or a related field, or equivalent years of experience; listed as 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.
Demonstrated experience managing a roadmap and delivering data science or ML features end to end, with measurable business outcomes in planning, operations, or customer-facing domains.
Ability to work with data scientists and ML engineers and discuss modeling approaches, metric evaluation, 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 full ML lifecycle, including data preparation, model development, evaluation, deployment, and monitoring.
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 with 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, such as demand sensing, replenishment optimization, size curve modeling, or markdown optimization.
Familiarity with customer science, 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.
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 and experience bringing new capabilities into production with technical teams.

Responsibilities

Maintain a current, sequenced, and visible product roadmap across planning science and consumer AI, balancing near-term delivery with longer-term model and platform investments.
Work with merchandising, supply chain, finance, marketing, e-commerce, and technology partners to identify high-value data science and ML opportunities and prioritize them.
Facilitate trade-off discussions between business stakeholders and data science teams, and communicate roadmap status, sequencing changes, and delivery progress.
Identify dependencies, data readiness gaps, and model risks early.
Translate business planning, operational, and customer needs into product requirements, model evaluation criteria, and acceptance standards.
Partner with data scientists and ML engineers throughout development to clarify scope, review model outputs, align on evaluation frameworks, and adapt requirements as needed.
Ensure model outputs are integrated into usable business workflows and decision tools.
Run agile ceremonies and delivery practices to align cross-functional data science, engineering, analytics, and business teams.
Manage launches end to end, including rollout plans, user enablement, stakeholder communications, and post-launch monitoring.
Apply product delivery practices to agentic AI and generative AI projects when relevant.
Build relationships with merchandising, planning, supply chain, finance, marketing, and CRM teams to understand workflows, data environments, and decision needs.
Support customer and consumer analytics initiatives, including lifetime value modeling, segmentation, churn prediction, propensity scoring, and personalization.
Work with data science and analytics teams to monitor production model accuracy, drift, coverage, and downstream business impact.
Use stakeholder feedback, model performance data, and user behavior to maintain a continuous improvement backlog.
Advocate for interpretable, trustworthy model outputs that fit the decisions users need to make.
Provide senior leadership with regular progress updates and outcome summaries grounded in model performance metrics and business results.