Senior Business Analyst – Report Writing Platform for Valuation & Advisory

Senior Business Analyst – Report Writing Platform for Valuation & Advisory

Contract to Hire role

Remote

The problem we’re solving

• Too much of a producer’s time goes to assembling reports (pulling data from multiple sources, reconciling it, and shaping it into narrative) and too little to the analysis and professional judgment the work exists for.
• Producers’ expertise is what makes this work valuable. We are removing the assembly around it, not replacing the judgment.
• We are rebuilding our report writing platform so that assembly is largely automated, with producers reviewing, deciding, and signing off. We need a Senior Business Analyst who questions how reports get written today, tests new ideas early, and turns what works into a delivered, measurable capability. The solution will be used globally, so you will work closely with teams and stakeholders across regions.
• This is not a documentation role. If your instinct is to ask whether a step should exist at all before asking how to automate it, read on.

What success looks like in 12 months

• By the end of your first year, we expect you to have driven outcomes like these:
• Reduced average producer time from assignment to completed draft report by 50%, by redesigning the workflow rather than digitizing the existing one.
• Shipped AI-assisted capabilities into production with measured accuracy and producer adoption.
• Retired or eliminated steps that no longer add value, and can show the before-and-after.
• Run pilots early: validated or killed ideas with real producers and real data before they reached the engineering backlog.
• Established an evaluation approach (test sets, quality thresholds, human review rates) that the team uses to decide when an AI output is trustworthy.
• Built a benefits-tracking view that leadership uses to see productivity, quality, and risk results.
• Earned the trust of producers and SMEs, who help set quality standards and report that the platform improves their work.

What you’ll do

• You will own the business analysis for the platform, but your measure is outcomes delivered, not documents produced.
• Reimagine how reports get written
• Study where producers actually spend their time, then redesign the end-to-end workflow from source data to signed report, starting from the question “what would we build if we began from scratch?”
• Test assumptions with stakeholders and subject matter experts, using data and prototypes, and work toward shared decisions.
• Identify and prioritize AI and automation opportunities (GenAI and LLMs, intelligent document processing, agents, workflow orchestration) by business value, feasibility, risk, and measurable outcome. Favor eliminating or redesigning a step over automating it as-is.
• Build business cases and decide, with product and engineering, what to pursue, pilot, or drop.
• Prototype and validate before you specify
• Test ideas hands-on with GenAI tools: draft and refine prompts, run sample report data through them, and build small evaluation sets.
• Run small pilots with producers to learn what works before committing engineering capacity.
• Define how AI output is judged: grounding in approved sources, confidence scoring, output validation, prompt management, and when a human must review.
• Partner with producers and business SMEs as co-designers: use their expertise to define quality standards and evaluation criteria for AI output, and make sure they approve what ships.
• Define the platform and its controls
• Define requirements for dynamic templates, reusable content libraries, standardized narratives, automated calculations, and configurable workflows.
• Define human-in-the-loop review, approval, traceability, exception handling, and fallback paths so producers keep professional judgment and accountability.
• Define data requirements: source mapping, validation, lineage, and business rules for missing, conflicting, outdated, or unverifiable information. Partner with data engineering to make that data reliably available.
• Deliver with the team
• Lead discovery workshops and stakeholder sessions, resolve conflicting requirements, and drive timely decisions across business, product, technology, data, and governance.
• Produce the standard BA artifacts (epics, user stories, acceptance criteria, process flows, business rules, traceability) as needed to support agile delivery; they are a means, not the goal.
• Define edge cases, exception paths, and negative tests for both traditional and AI-enabled features. Support UAT, defect triage, and release readiness.
• Support pilots, demos, training, and adoption, and bring producer feedback back into the roadmap. Communicate how new controls and automation change day-to-day work.
• Work with global business, product, technology, and data teams across time zones. Gather requirements from multiple regions and balance local differences (standards, templates, language, regulation) against a consistent global platform.

What you’ll bring

• Proven impact
• A track record of reimagining a process, not just documenting it, with measurable results you can describe (cycle time, quality, cost, or adoption).
• Experience where you chose to eliminate, redesign, or abandon something rather than automate it as-is, and can explain why.
• Comfort testing assumptions with stakeholders and SMEs, including respectfully pushing back on a stated requirement that solved the wrong problem, backed by data.
• Hands-on AI experience
• Practical experience using or prototyping with GenAI and LLM tools to test ideas and validate use cases before writing formal requirements.
• Working knowledge of intelligent document processing and AI-assisted workflow automation, and of how AI output is evaluated for accuracy, grounding, and risk.
• Ability to prioritize AI use cases by business value, feasibility, risk, and measurable outcome, and to say no to the ones that fail that test.
• Core business analysis and delivery
• 7+ years in business analysis, product delivery, process transformation, or enterprise application modernization.
• Strong requirements engineering, process modeling, and Agile delivery skills, including business rules, data mappings, integrations, validation logic, and exception handling.
• Experience with document-intensive, report-generating, or workflow-heavy enterprise platforms.
• Experience supporting UAT, defect triage, release readiness, and production implementation.
• Excellent analytical, facilitation, communication, and stakeholder management skills, working across business, product, engineering, data, and AI/ML teams.
• Experience working with globally distributed teams and stakeholders, collaborating effectively across regions, time zones, and cultures.

Tools and data skills

• Working knowledge of Excel, Word, and SQL
• Experience with a work-management tool such as Smartsheet (or similar) and a DevOps or work-tracking tool such as Azure DevOps (or similar) to plan work, manage the backlog, and track delivery.
• Knowledge of Power BI (or similar) to read dashboards and data models and understand how the current-state process performs. This is for understanding today’s process; it is not a requirement for the future product.
• Experience creating process maps and workflow diagrams with a tool such as Visio, Lucidchart, or draw.io (or similar), including BPMN or similar notation.
• Hands-on use of GenAI and LLM tools (such as Copilot, ChatGPT OR Claude, or similar) to prototype ideas, test prompts, and build small evaluation sets.

Nice to have

• Experience in real estate appraisal, property valuation, or appraisal management, or in another document-heavy, judgment-driven, regulated domain (insurance underwriting, audit, legal, or clinical documentation, for example). Fresh patterns from adjacent fields are welcome.
• Familiarity with appraisal reports, valuation methodologies, and applicable industry standards.
• Experience with AI-powered document generation, enterprise knowledge search, or AI copilots.
• Exposure to workflow orchestration, APIs, cloud platforms, and enterprise data architectures.
• Familiarity with responsible AI practices: model evaluation, human oversight, explainability, and governance.
• Experience defining business metrics, dashboards, operational KPIs, and benefits-realization frameworks.
• Examples of work you have built or tried on your own, such as a prompt library, an evaluation set, or a working prototype.
• Familiarity with Postman (or similar) and the ability to read APIs and API documentation.
• Experience with a wireframing or prototyping tool such as Figma (or similar) to mock up workflows or screens for pilots and user feedback sessions.