AI Business Intelligence Analyst

iQor; Metro Manila; On-site; PHP 600,000 - 1,000,000; Full time

Job summary

iQor is seeking an AI Business Intelligence Analyst - HR & Talent Acquisition to transform workforce, candidate, recruitment, and recruiter-performance data into trusted dashboards and automated reporting. You will own the full reporting lifecycle, from requirements gathering to executive communication, partnering with HR, TA, and operations across the enterprise.

In this role, you will build HR and TA dashboards, perform AI-assisted analysis, and develop workforce planning insights.

Qualifications

• 2+ years of BI, analytics, or recruiting analytics experience.
• Portfolio of dashboards and automated reports.
• Proficient in SQL and at least one enterprise BI tool.
• Experience with AI in analytics and data privacy awareness.

Responsibilities

• Design, build, publish, and maintain HR and recruiting dashboards and scorecards.
• Develop end-to-end recruitment funnel reporting across stages.
• Create data models, DAX measures, and segmentation for insights.
• Provide concise insight narratives for executive reviews.

Skills

BI / Analytics experience

SQL

Spreadsheet proficiency

Data visualization design

Communication

Education

Bachelor's degree in analytics/CS/IB/HR analytics

Tools

Power BI

Tableau

SQL databases

Job description

The AI Business Intelligence Analyst - HR & Talent Acquisition converts workforce, candidate, recruitment, and recruiter-performance data into trusted dashboards, automated reporting, operational intelligence, and business recommendations. The role owns the full reporting lifecycle: requirements gathering, data validation, modeling, visualization, AI-assisted analysis, insight development, and executive communication.

The analyst partners with Talent Acquisition, HR, Operations, Workforce Management, Finance, Technology, and site leadership to identify hiring risks early, improve funnel conversion, strengthen recruiter effectiveness, reduce manual reporting, and support evidence-based workforce decisions

Primary Business Outcomes

• Create one trusted source of truth for Talent Acquisition and priority HR performance measures.
• Give leaders early visibility into class-fill risk, funnel leakage, candidate fallout, recruiter capacity, and workforce demand.
• Shorten the time from data extraction to business action through automation and AI-assisted analysis.
• Improve the quality and consistency of operational reviews, recruiter coaching, workforce planning, and executive decisions.
• Establish responsible, human-reviewed use of generative AI across HR and recruiting analytics.

Key Accountabilities

1. HR and Talent Acquisition Business Intelligence

• Design, build, test, publish, and maintain HR and recruiting dashboards, scorecards, exception reports, and business-review packs.
• Develop end-to-end recruitment funnel reporting covering demand, requisitions, leads, contact attempts, screening, assessments, interviews, offers, requirements completion, onboarding, Day 1 attendance, and early-tenure outcomes.
• Build workforce and people dashboards covering headcount, movement, attrition, absenteeism, retention, staffing gaps, productivity, and other approved HR measures.
• Create data models, DAX measures, drill-through views, cohort analyses, segmentation, trend views, and recruiter or site-level performance cuts.
• Translate HR and TA operating questions into clear business requirements, KPI definitions, calculation rules, report logic, and visualization designs.
• Document data sources, metric owners, assumptions, refresh schedules, dependencies, filters, and approved sources of truth.

2. Recruiting Insights and Decision Support

• Identify class-fill risk, lead aging, funnel bottlenecks, conversion gaps, candidate fallout, no-show patterns, recruiter workload imbalance, and sourcing-channel performance.
• Explain what happened, why it happened, what is likely to happen next, and which intervention should be prioritized.
• Develop hiring forecasts, funnel survival models, capacity scenarios, source-mix analysis, recruiter productivity views, and location or talent-pool intelligence.
• Connect recruiting outcomes to operational demand, training readiness, start-date requirements, early-tenure retention, and approved quality-of-hire indicators.
• Produce concise insight narratives for daily stand-ups, weekly operating reviews, executive reviews, workforce planning, and client discussions.
• Convert findings into recommended actions with owners, deadlines, expected impact, and follow-through measures.

3. AI Prompting and Conversational Analytics

• Create reusable prompts and AI-assisted workflows that summarize recruiting performance, identify patterns, classify root causes, and draft evidence-based report commentary.
• Analyze approved candidate and recruiter interaction data to identify call behaviors, candidate objections, unresolved questions, process misses, and coaching opportunities.
• Develop prompt templates, evaluation rubrics, test cases, and human-in-the-loop review controls for recurring HR and TA use cases.
• Maintain a governed prompt library that documents approved inputs, intended use, expected output, limitations, validation steps, ownership, and version history.
• Test AI outputs for factual accuracy, unsupported conclusions, bias, privacy exposure, inconsistent logic, and sensitivity to prompt wording or incomplete data.
• Ensure AI supports not replaces recruiter, HR, and leadership judgment. The role does not authorize fully automated employment decisions.

4. Reporting and Workflow Automation

• Automate recurring data preparation, report refresh, commentary generation, distribution, exception alerts, and approved follow-up workflows.
• Integrate or reconcile data from ATS/CRM, HRIS, assessment, telephony, quality, workforce, finance, onboarding, and learning systems.
• Reduce spreadsheet dependency, duplicate reporting, manual consolidation, and inconsistent calculations while preserving clear control points.
• Develop proactive alerts that identify at-risk candidates, aging requisitions, unfilled classes, unusual conversion shifts, missing requirements, or data-quality failures.
• Track hours saved, defects reduced, cycle-time improvement, user adoption, and business value generated by automation.

Data Quality, Privacy, and Responsible AI

• Validate source completeness, reconcile conflicting figures, investigate anomalies, and prevent unverified information from reaching decision-makers.
• Maintain an HR and TA metric dictionary with definitions, inclusions, exclusions, calculation rules, ownership, and approved sources.
• Protect employee, candidate, client, compensation, performance, and personally identifiable information through appropriate access, minimization, and retention controls.
• Use only approved AI tools and approved data inputs; do not place confidential HR or candidate data into unapproved public AI services.
• Clearly distinguish verified findings, analytical estimates, AI-generated hypotheses, and areas where the evidence is insufficient.
• Partner with Privacy, Security, Legal, HR, and Technology teams on governance, access, auditability, and appropriate use of analytics in employment decisions.

Stakeholder Partnership and Enablement

• Partner with TA leaders, recruiters, sourcers, onboarding teams, HRBPs, Operations, Workforce Management, Finance, Training, and technology owners.
• Facilitate requirements sessions and challenge requests that produce activity reports without a clear decision or business use.
• Train leaders and end users to interpret dashboards, use metric definitions correctly, ask stronger analytical questions, and apply approved AI workflows safely.
• Monitor report adoption and stakeholder feedback; improve high-value products and retire low-value or duplicative reports.
• Present findings confidently to operational and executive audiences, including uncomfortable findings that the evidence supports.
• TA command-center dashboard covering demand, funnel, conversion, class-fill confidence, source performance, aging, and risk.
• Daily and weekly exception reporting for leads, assessments, interviews, offers, requirements, onboarding, and Day 1 attendance.
• Recruiter and team performance scorecards with productivity, conversion, quality, aging, adherence, and coaching context.
• Candidate-call analytics covering behaviors, objections, unresolved concerns, process adherence, and coaching opportunities.
• HR workforce dashboards for approved headcount, attrition, movement, attendance, retention, capacity, and workforce-planning measures.
• Executive insight summaries with verified findings, implications, recommended actions, owners, and measured impact.
• AI prompt library, evaluation tests, control documentation, and human-review procedures.
• Metric dictionary, data lineage notes, report inventory, refresh calendar, access matrix, and reporting roadmap.

Required Qualifications

• Approximately two or more years of relevant experience in BI development, people analytics, recruiting analytics, reporting, operations analytics, or a closely related discipline. Equivalent demonstrated capability may substitute for tenure.
• A portfolio showing dashboards, data models, automated reports, or analytical products built personally not merely reports consumed or coordinated.
• Demonstrated capability in SQL, spreadsheets, data preparation, and at least one enterprise BI platform.
• Practical experience using generative AI for structured analysis, prompting, workflow acceleration, text analysis, or report development.
• Working knowledge of recruitment funnels, HR data, operational KPIs, data privacy, and the sensitivity of employment-related information.
• Strong business writing, visualization, presentation, and stakeholder-management skills, including the ability to explain findings to nontechnical leaders.
• Evidence of analytical judgment, data skepticism, quality control, ownership, and comfort working with incomplete or conflicting requirements.

Preferred Qualifications

• Direct experience supporting Talent Acquisition, HR, workforce operations, high-volume hiring, BPO, shared services, or multi-site operations.
• Experience combining ATS/CRM, telephony, assessment, HRIS, onboarding, and operational data into one reporting model.
• Experience automating recurring reports or analytical workflows through low-code tools, APIs, SQL, or Python.
• Exposure to call or text analytics, forecasting, predictive analytics, natural-language-to-SQL, retrieval-based AI, or AI agents.
• Relevant certification in Power BI, Tableau, analytics, cloud platforms, process improvement, data engineering, HR analytics, or responsible AI. Certifications do not replace a work sample.
• Bachelor's degree in analytics, information systems, computer science, statistics, economics, industrial engineering, psychology, human resources, business, or a related field; equivalent practical experience is acceptable.