AI Business Analyst

The AI Business Analyst sits within Jazwares' IT organization on the AI Team and serves as the team's business translation and requirements engine, turning broad business problems into clear, actionable AI project requirements. While the reporting structure is technical, this role is best understood as an internal innovation role - the AI Team is building production machine learning systems for pricing, demand forecasting, inventory, and supply chain optimization, and document intelligence systems for licensing, SOPs, vendor governance, and product data. The success of these initiatives depends on tight alignment between business stakeholders and the data scientists and engineers building the systems, and the AI Business Analyst owns that alignment. Reporting to the Senior AI Product Manager, the AI Business Analyst conducts structured interviews across Legal, Sales, Finance, Supply Chain, Operations, Product, and IT; documents current-state workflows and pain points; defines use cases, KPIs, and success metrics; identifies required data sources and quality issues; and helps quantify expected business value. They support pilot validation and launch readiness, and they uphold the team's foundational prioritization of data readiness. This role offers meaningful exposure to applied machine learning, LLM-based document intelligence, and enterprise-scale data systems, working closely with Data Analysts, Lead AI Scientist, Project Manager, Product Leadership, and Department Leaders on projects that touch nearly every part of the company. It is a high-visibility seat for someone who is analytically sharp, comfortable with ambiguity, energized by enterprise transformation, and comfortable in fast-moving and loosely defined environments. This role also calls for someone willing to extend beyond a narrow role definition when the work calls for it and naturally drawn to work where business judgment and technical fluency meet.

Duties and Responsibilities

Stakeholder Discovery and Interviewing: Conduct structured interviews across Legal, Sales, Finance, Supply Chain, Operations, Product, and IT to surface candidate AI use cases, understand existing workflows, and capture pain points, constraints, and success criteria.

Requirements Definition: Translate business needs into clear, technically actionable use cases, requirements documents, user stories, and acceptance criteria that data scientists and engineers can build against.

Workflow and Process Mapping: Document current-state workflows, business rules, and decision logic for each priority use case, then map proposed future-state flows that incorporate AI/ML components.

Data Discovery and Readiness Assessment: Identify required data sources, owners, gaps, and quality issues for each project, recognizing that data readiness is the gating factor for most AI initiatives on the roadmap.

Business Value Quantification: Build the case for each project by quantifying expected impact in terms of time savings, cost avoidance, revenue lift, or operational efficiency, and by defining the KPIs the team will use to measure outcomes.

Pilot Validation and Launch Readiness: Support proofs of concept and pilots through user testing, feedback collection, acceptance-criteria validation, and readiness checks before broader rollout.

Cross-Functional Project Coordination: Partner with data scientists, ML engineers, project managers, and business owners to keep projects grounded in practical business needs, tracking risks, dependencies, open questions, and decision points.

Executive-Ready Documentation: Produce business process maps, requirements documents, project summaries, and concise executive briefings that allow leadership to make informed decisions on scope, sequencing, and investment.

Communication: Maintain clear, timely written and verbal communication across business stakeholders and technical contributors, ensuring all parties understand current status, open questions, and next steps.

People Leader: No

The AI Business Analyst does not have direct reports. The role does carry meaningful cross-functional leadership responsibility - facilitating interviews, driving alignment between business and technical teams, and serving as the connective tissue that keeps multi-stakeholder AI initiatives moving forward. They are expected to act with ownership and judgment in keeping projects on track, and to communicate effectively up to the Senior AI Product Manager and across business and technical contributors at all levels of the organization.

Education:

Required: Bachelor's Degree

Preferred: Master's Degree

Required Level/Type/Years of Experience

Educational Background: A bachelor's degree in Business, Economics, Information Systems, Data Analytics, Operations, Engineering, or a related field is required.

Relevant Work Experience: A minimum of 2-5 years of experience in business analysis, business systems analysis, operations analysis, consulting, product operations, analytics, or digital transformation.

Stakeholder Requirements Gathering: Demonstrated experience interviewing cross-functional stakeholders and translating their needs into clear written requirements, user stories, or specifications.

Workflow Documentation: Experience documenting current-state workflows, business rules, and process gaps in a way that both business and technical audiences can act on.

Data-Heavy Problem Solving: Comfort working with data-heavy business problems, including reasoning about data sources, structures, and quality considerations relevant to analytics or modeling work.

Technology Project Support: Experience supporting technology, analytics, automation, or enterprise software projects, ideally through the full lifecycle from discovery through launch.

Spreadsheet Proficiency: Strong working proficiency in Excel or Google Sheets, including pivot tables, lookups, and complex formulas, with the ability to structure and analyze datasets quickly.

Preferred Level/Type/Years of Experience

Prior experience in the toy or consumer products vertical is highly valued, as is exposure to adjacent verticals such as retail, supply chain, licensing, legal operations, finance, or e-commerce. Familiarity with enterprise systems such as ERP, WMS, POS, BI, CRM, DAM, PIM, or workflow tools is also valuable. Any direct exposure to AI, machine learning, LLMs, automation, or data products - even at a project-support level - is considered a meaningful asset.

Required Knowledge, Skills, Abilities, & Other Characteristics (KSAO's)

Requirements Gathering and Documentation: Skilled at eliciting requirements from non-technical stakeholders and writing them in clear, unambiguous language that engineers can build against.

Workflow and Process Mapping: Able to map complex, multi-step business processes into structured visualizations that surface decision points, handoffs, and gaps.

Business Case and KPI Development: Capable of defining success metrics, baseline measurements, and target outcomes that allow projects to be evaluated objectively.

Data Literacy and Analytical Reasoning: Comfortable reading data, identifying patterns, asking sharp questions about data quality, and reasoning about cause and effect.

Stakeholder Interview Facilitation: Strong facilitation skills - knowing when to ask, when to listen, and how to keep a conversation focused without shutting down useful tangents.

User Story and Acceptance Criteria Writing: Able to convert use cases into well-formed user stories with clear, testable acceptance criteria.

Spreadsheet Skills: Strong Excel and spreadsheet skills, including pivot tables, lookups, charting, and lightweight modeling.

Translation Across Audiences: Able to translate business needs into technical-friendly specifications and translate technical concepts back into business-friendly language.

Written Communication: Clear, concise written communication tuned to the audience - short for executives, precise for engineer(s), complete for documentation.

Structured Thinking: Approaches ambiguous problems with structure, breaking them into testable definitions and prioritized work.

Detail Orientation: Catches inconsistencies, missing fields, and edge cases before they become production issues.

Intellectual Curiosity: Genuinely interested in how the business works and how new tools - including AI - can change it; willing to dig into unfamiliar domains rather than wait to be briefed.

Comfort with Ambiguity: Operates effectively in early-stage, loosely defined problem spaces where the right answer is not yet obvious.

Cross-Functional Communication: Works effectively with business, technical, and executive audiences, adjusting tone and depth as needed.

Practical Judgment and Business-Value Orientation: Consistently makes choices that move work toward measurable business outcomes rather than activity for its own sake.

Conscientiousness and Ownership: Takes full responsibility for the quality of the work, follows through on commitments, and is willing to extend beyond a narrow role definition when it serves the team and the project.

Preferred Knowledge, Skills, Abilities, & Other Characteristics (KSAO's)

 Basic SQL or BI Tool Experience: Working familiarity with SQL or BI platforms such as Sigma, Power BI, or Tableau, sufficient to query data and build basic reports independently.

Machine Learning Concepts: Working understanding of core ML concepts including training data, features, labels, model outputs, validation, and model drift.

LLM Concepts: Working understanding of LLM concepts including source documents, citations, retrieval, metadata, human-review workflows, and hallucination risk - particularly relevant to the team's principle that source text remains the source of truth.

Project Tooling: Experience with tools such as Jira, Confluence, Smartsheet, Lucidchart, or Miro for tracking work and visualizing processes.

Pilot and Proof-of-Concept Experience: Prior experience supporting pilots, proofs of concept, or analytics product rollouts where requirements evolved during build.

Agile Project Practices: Familiarity with Agile project practices and rituals, including standups, sprint planning, and backlog grooming.

Executive-Ready Summaries: Proven ability to distill complex, multi-stakeholder work into one-page executive summaries that drive decisions.

What we offer:

The salary range for this position is $86,000 - $105,000/ Yr. The base salary may vary based on experience, role tenure, performance, industry, and location. Eligibility for the annual performance incentive may apply. Jazwares is a multi-state employer, so the salary range may not apply to other states.  

Our benefits package includes basic medical insurance that is 100% company-paid for employees and their children, employee basic life and AD&D insurance, a 401(K) retirement program with Jazwares matching up to 4% of pretax or post-tax deferrals, short and long-term disability, and tuition reimbursement.   

Our work environment provides a flexible work schedule that includes Monday through Thursday on-site, with an optional WFH on Fridays, up to 20 workdays fully remote each year, and Time Off for vacation and sick leave.  Through Jazwares Cares, you will have the opportunity to volunteer for up to 16 hours a year on community service projects. 

Working at Jazwares