Intelligence Products and Systems Manager

World Business Lenders

1d•Remote

About The Position

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.

Requirements

Twelve or more years of progressive experience across applied AI/ML, data science, software, analytics, decision systems, or technical product development, including at least five years of people leadership and meaningful experience leading Team Leads, managers, or senior technical/product staff.
Must have demonstrated delivery of AI-enabled or model-driven capabilities into real production use.
Demonstrated success building, scaling, or materially improving an applied intelligence, AI product, decision-product, or technical product capability.
Must be able to coach Team Leads, develop technical/product talent, own and prioritize a portfolio, write clear product requirements and acceptance criteria, and deliver usable production products with cross-functional teams.
Must have experience making funding, sequencing, and trade-off decisions with senior business leaders.
Recent hands-on product and technical delivery is required; this is not a management-only role.
Experience taking AI-enabled or analytical products from ambiguous business problem through discovery, business case, prototype, requirements, acceptance criteria, production launch, user adoption, monitoring, and iterative improvement.
Experience with modern AI product development and evaluation, including model selection, API-based AI services, structured evaluation, retrieval/grounding, LLM workflow design, human-in-the-loop controls, and production observability, alongside meaningful applied AI experience beyond generative AI.
Experience building decision-support or decisioning products that combine data, calculations, rules, models, external data sources, and software into a repeatable user-facing capability.
Broad applied AI experience selecting and evaluating the right approach for a business problem, including predictive machine learning, rules and decision engines, optimization, document understanding/extraction, NLP, generative AI/LLMs, retrieval, agents or tool-using workflows, and hybrid human-in-the-loop systems as appropriate.
Demonstrated production integration, monitoring, user adoption, and measurable business outcomes for AI-enabled or model-driven products.
Strong understanding of model and product risk, evaluation design, explainability, privacy, security, data quality, access controls, monitoring, drift or degradation, exception handling, human oversight, and responsible use.
Able to communicate technical choices and risk clearly to senior business leaders and translate business requirements into precise direction for data science and engineering teams.
Ability to distinguish product ownership from data science and software engineering ownership: define the problem, user experience, requirements, acceptance criteria, operating controls, and outcomes while holding specialist teams accountable for model evidence and software implementation within their domains.
The Manager is expected to lead product discovery, develop prototypes, evaluate applied AI approaches, define product behavior and acceptance criteria, challenge model evidence, and investigate performance issues.
The Manager must also build a durable organization through hiring, coaching, delegation, roadmap discipline, standards, performance management, and development of Team Leads.
Team Leads contribute substantively to delivery as well as supervise their teams.

Nice To Haves

Relevant education or professional training in computer science, engineering, data science, mathematics, product management, or a related technical discipline is valued.
Generative AI experience is valuable but is not sufficient by itself; candidates must show broader applied-AI judgment and enough technical depth to challenge model evidence, limitations, and suitability for intended use.
Experience in lending or financial services, business-user support, and data reconciliation preferred.

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.
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.