Digital Content Specialist, Professional Senior

Implement, configure, and optimize knowledge and AI content tooling within assigned areas. Proactively identify tooling and process improvement opportunities based on performance data, and recommend enhancements with clear expected impact to deliver measurable improvements in content delivery.

• AI Performance & Optimization:

Independently monitor AI content performance signals - including retrieval gaps and answer quality - and assess AI-generated responses for accuracy, grounding, and consistency. Apply human-in-the-loop validation, direct corrective actions, and continuously write, test, and refine prompts to improve how knowledge is surfaced.

• Knowledge Gap Execution & Content Direction:

Drive the knowledge gap strategy by conducting regular audits to identify outdated or underperforming content. Provide clear, prioritized direction to content owners and SMEs on required updates and fixes, tracking resolution to ensure measurable improvement and alignment with defined standards.

• Standards, Discoverability & Localization:

Uphold content standards, metadata schemas, and taxonomy frameworks, identifying gaps and driving updates through appropriate governance channels. Apply SEO and AI discoverability principles (GEO, AEO) to optimize retrieval, while ensuring content structures enable high-quality localization and flagging systemic issues for resolution.

• Cross-Functional Collaboration & Validation:

Partner with Product, Engineering, Content Management, KM Operation, and Support Operation teams to ensure knowledge initiatives are accurate, timely, and contribute to support efficiency. Continuously validate that improvements result in better self-service success, reduced support friction, or an improved agent experience.

• Mentorship & Team Development:

Provide day-to-day direction, guidance, and support to junior team members, ensuring the overall quality of team output and supporting their professional development within the digital content field.

Bachelor's degree in Information Science, Information Management, Computer Science (AI/Data Science track), or a related field, or equivalent practical experience.

• Experience:

3-5 years of experience in content strategy, content operations, or knowledge management within an enterprise or high-volume support environment.

• Retail/E-commerce Domain Knowledge:

Solid understanding of how structured content and product information drive discoverability and self-service outcomes at scale within retail or e-commerce operations.

• AI & Systems Expertise:

Demonstrated hands-on experience with AI-powered content tools (e.g., RAG, Copilots, or Chatbots). Must understand how structured content and metadata influence answer quality and retrieval optimization.

• Prompt Engineering:

Proven ability to write, test, and refine prompts and guidance for AI tools to improve how knowledge is surfaced in support experiences.

• Content Governance:

Experience executing within knowledge governance frameworks - applying content standards, metadata schemas, and taxonomy principles across large-scale repositories.

• Data-Driven Improvement:

Experience using content analytics and performance signals (such as retrieval gaps or self-service impact) to independently identify issues and drive targeted improvements.

• SEO & Discoverability:

Working knowledge of SEO and AI discoverability concepts, specifically GEO and AEO.

• Technical Writing:

Strong technical writing skills with the ability to translate complex information into clear, user-friendly content.

• Collaboration & Influence:

Proven ability to provide direction to content owners and SMEs, ensuring alignment with standards and timely execution of updates.

• Mentorship:

Experience (formal or informal) providing guidance to more junior team members or leading specific project workstreams.

Familiarity with Knowledge-Centered Service (KCS) methodology.

• Localization:

Experience supporting localization quality and ensuring content structures enable effective translation across multiple languages.

• Scale:

Experience supporting AI-driven knowledge workflows, such as automated classification or large-scale content optimization.