Intelligence Products and Systems Manager

Intelligence Products and Systems Manager

BLV Applied Intelligence Products and Decision Systems

• Reports to: Chief Data and Analytics Officer
• Department: IDEA — Intelligence, Data, Engineering & Analytics
• Team structure: Two Team Leads and four Analysts across BLV and Applied Intelligence Products

Position Summary

• The Intelligence Products and Systems Manager is the senior hands-on product leader for BLV and WBL's broader portfolio of applied intelligence products. Reporting to the CDAO, this role manages through two Team Leads, each responsible for two Analysts.
• One team owns BLV, the corporate valuation product; Applied Intelligence Products owns the broader portfolio of AI-enabled, model-driven, rules-based, and decision-support products. The Manager identifies high-value business problems, determines where AI/ML, rules, optimization, document intelligence, or other approaches are appropriate, and leads products from discovery and business case through requirements, acceptance, deployment, adoption, monitoring, improvement, and retirement.
• The role requires enough technical depth to prototype, evaluate approaches, challenge evidence, and diagnose product performance while working across Data Science, Data Engineering, Software Engineering, Business Applications, and business owners.

Core Responsibilities

• Lead Intelligence Product Teams
• Manage, coach, and develop two Team Leads and four Analysts, with clear ownership, technical standards, feedback, and accountability.
• Own and prioritize product roadmaps based on user needs, business value, feasibility, data readiness, operational risk, and capacity, with clear success measures and acceptance criteria for each initiative.
• Own the product portfolio and resource plan; hire, assess performance, develop Team Leads, and build succession coverage for critical product capabilities. Maintain regular hands-on involvement in priority delivery.
• Build and Operate Intelligence Products
• Oversee product discovery, prototyping, acceptance, deployment into business use, monitoring, improvement, and retirement. Analytics / Data Science owns analytical methods, predictive model development, and model-performance evidence; Software Engineering owns application architecture, implementation, and release quality.
• Ensure each product integrates into business processes, supports appropriate human oversight and user adoption, and delivers measurable outcomes. Own product acceptance against agreed criteria; business owners approve policy, intended use, and operating decisions. Coordinate user enablement with Business Applications.
• Develop product business cases and lead discovery through adoption; use evidence of value, feasibility, and risk to recommend which products to fund, scale, improve, or retire.
• Govern Models and Systems
• Establish product controls for evaluation, documentation, explainability, privacy, security, change control, monitoring, and responsible use. Coordinate any required independent validation and business approval separately from development and product acceptance.
• Own product performance monitoring, exception triage, escalation, and corrective-action follow-through. Route model issues to Data Science, data issues to Data Engineering, and software defects to Software Engineering; coordinate user communication with Business Applications.
• Review product outcomes with senior business leaders, challenge weak performance evidence, and adjust priorities and controls as user needs and operating conditions change.