Senior Manager Analytics

About the role

You will spearhead the evolution of data and sales operations at cabi, ensuring that every strategic decision is grounded in precise, actionable insight. In this capacity, you will define and own the analytical frameworks that connect sales goals with the operational reality of our field teams. You will architect the data infrastructure required to transform raw information into a strategic asset for the business. This role demands fluency in both the human elements of sales leadership and the technical nuances of modern data platforms. You will leverage artificial intelligence not as a novelty, but as a core mechanism for automating analysis and accelerating decision velocity. Your work will directly influence how our Field Strategy team optimizes processes and how our merchandising team understands consumer behavior. You will be the guardian of data quality, ensuring that the stories we tell ourselves about performance are accurate and reliable. Ultimately, you will shape the analytical culture of the sales organization, driving a future where insights are generated faster than ever before.

Key facts

What you'll do

Analyze the performance of company KPIs to date, pinpointing significant opportunity areas within the sub-components of sales and community targets.

Design and maintain scalable data architecture for new and supplemental data sources, constructing data models, schemas, and pipelines that extend our core infrastructure.

Collaborate with engineering to govern data standards and documentation, ensuring new sources align with existing conventions and source-of-truth datasets.

Leverage AI tools to accelerate analysis, surface insights, and streamline reporting workflows, evaluating new capabilities for sales and field strategy.

Define and implement processes at scale to improve the efficiency and quality of the Stylist experience.

Partner with the merchandising team to deepen seasonal sell-through through unique calls to action and data analysis highlighting actionable consumer behaviors.

Partner with the training team to strengthen training content and assess its impact on sales performance and knowledge retention.

Provide daily, weekly, and monthly sales forecasting, maintaining and validating models with the latest internal and external data to improve accuracy continuously.

Oversee projects that answer business questions posed by cross-functional partners, translating ambiguity into structured analytical work.

Pursue lead generation opportunities that expand the Stylist and home office audience reach, supporting sustainable growth.

Optimize technology to support sales processes, creating improved support workflows and efficient knowledge transfer via back-office tools and training content.

Consistently review core bonus and compensation programs to ensure ongoing alignment between reward behaviors and priority company objectives.

Promote collaboration between field teams and analytics, aiding in the optimization of technology to support efficient sales processes and knowledge transfer.

Define metrics and success criteria for new initiatives, ensuring that the impact of analytics and field strategy work is measurable and understood.

Requirements

Demonstrate fluency in the use of AI tools to enhance productivity, automate routine analysis, and drive faster, more informed decision-making across the sales organization.

Own the extension of data infrastructure for sales and field strategy insights, focusing on new sources that directly support analytics and reporting capabilities.

Work closely with the engineering team on data governance standards and documentation practices for new data sources.

Possess the ability to translate complex analytical findings into clear, actionable guidance for field teams and senior management.

Apply cohort analysis to identify where universal messaging is appropriate versus opportunities for customized aids or guidance targeted toward segmented groups.

Collaborate with senior management on seasonal and monthly planning for sales targets, as well as promotion and incentive schedules.

Ensure strict adherence to data governance and source-of-truth standards as defined by the engineering organization.

Commit to the continuous improvement of data quality, reliability, and accessibility across all analytics platforms and reporting systems.