Onaolapo Odunjo

Lead production AI systems, enterprise deployment, evaluation infrastructure, and GTM automation for regulated financial workflows, including top 10 U.S. bank design partner workflows.

• ▸Defined 0→1 product strategy and enterprise roadmap for AI-powered decision intelligence platform, sequencing ingestion, classification, anomaly detection, workflow state management, analyst validation, GTM intelligence, and human review based on customer risk, technical dependency, and enterprise readiness.
• ▸Architected AI financial operating system managing lifecycle state from Detected → Categorized → Due → Paid → Reconciled; made core tradeoffs across multi-source ingestion, fuzzy-matching ETL, duplicate detection, backend state integrity, and human-in-the-loop approvals.
• ▸Built production AI evaluation infrastructure processing 500+ weekly signals into 5–10 high-confidence outputs used in 100% of enterprise sales conversations; improved precision 22% → 50% and reduced false positives 60% → 15% through 300+ labeled examples, error taxonomy, and confidence calibration.
• ▸Embedded two days/week with top 10 U.S. bank's risk and compliance team as enterprise design partner, translating live analyst workflows, edge cases, and decision criteria into model requirements, eval cases, and product acceptance thresholds.
• ▸Designed four-tier agentic workflow system: Signal Capture → Enrichment → Intelligence → Execution; centralized ICP scoring, account qualification, and next-best-action recommendations in Claude-powered governance layer exposed via API to Clay, HeyReach, and HubSpot.
• ▸Built transcript intelligence pipeline from Google Meet → Claude API → HubSpot, converting discovery calls into CRM-linked account context, qualification signals, and next-best-action recommendations.
• ▸Reduced AI workflow cost-to-serve by $17.9K/month (~$215K annually) by re-architecting tool boundaries, shifting classification from Clay to Claude API, and validating REST API integrations against native connectors.
• ▸Hired and managed 14-person contracted product, engineering, data operations, and evaluation team across Python/FastAPI backend, Next.js frontend, and GitHub Actions CI/CD.

Building credit infrastructure for Nigeria's informal economy by converting unstructured WhatsApp payment conversations into structured credit signals.

• ▸Built and launched WhatsApp-first NLP system for behavioral credit infrastructure in Nigeria's informal economy, converting unstructured Ajo payment conversations into structured credit signals and deriving creditworthiness from behavioral consistency patterns.
• ▸Reached 5K tester-phase customers in first two months by designing distribution strategy that embeds into existing WhatsApp workflows instead of requiring app downloads.
• ▸Created upstream data layer from informal trust networks that banks and lenders can underwrite against.

Owned ML-driven personalization and lifecycle strategy for Prime subscription retention across 1.17M+ MAU.

• ▸Owned product strategy for $148M ARR Prime subscription initiative serving 1.17M+ monthly active users within broader $8.4B business, translating churn analysis into activation, personalization, and lifecycle engagement roadmap.
• ▸Shipped ML-driven targeting framework for subscription personalization and retention, partnering with data science to define behavioral segments, measurement approach, and incremental lift analysis.
• ▸Instrumented usage tracking across subscription behaviors, analyzed conversion funnels and cohort patterns, and converted findings into roadmap priorities for first-week activation, habit formation, and benefit awareness.

Owned core enterprise data platform strategy and ML operationalization across fraud, lending, compliance, and analytics workflows for 15 business units.

• ▸Managed 13-person cross-functional platform team across PMs, engineers, BSAs, and data modelers; set roadmap priorities, operating cadence, requirements standards, and delivery accountability across 15 business units.
• ▸Built product strategy and investment case for $500M+ enterprise data platform by quantifying EBITDA impact of data reliability, compliance infrastructure, ML enablement, platform reuse, and capability sequencing; coordinated 170+ contributors.
• ▸Led 0→1 Azure data platform build after diagnosing structural fragmentation across source systems, metadata, and model inputs; defined canonical data model, metadata ontology, and governance architecture as system-of-record contracts for fraud, lending, and compliance.
• ▸Rebuilt executive trust after fraud-lending integration failure by tracing root cause to conflicting risk score definitions; introduced metadata registry, PII classification framework, and cross-BU governance working group.
• ▸Resolved VP-level escalation on real-time infrastructure by showing proposed architecture created 3x cost increase for <5% outcome improvement; defended hybrid model preserving detection performance while containing cost.
• ▸Operationalized fraud ML by standardizing feature pipelines and embedding model outputs into investigator workflows, eliminating manual triage across 5 systems, improving investigation throughput 29%, and reducing deployment time 30% across 40+ integrations.
• ▸Established ML deployment approval gates with Legal, Compliance, and fraud investigation teams, creating reusable governance standards for model readiness, PII handling, and operational adoption.

Owned product strategy for $270M enterprise API platform serving 200+ internal teams across consumer banking, commercial banking, and credit cards.

• ▸Drove 15% adoption growth and $80M+ measurable value creation by expanding platform capabilities, improving developer experience, and accelerating integrations across internal customers.
• ▸Built usage instrumentation tracking API consumption, error rates, integration bottlenecks, and downstream business impact; used signal data to prioritize roadmap investments toward highest-leverage platform capabilities.

Owned delivery for $14M Fortune 10 mobile banking engagement generating $15M revenue within three months of launch.

• ▸Translated enterprise requirements into technical specifications, managed stakeholder expectations across client organization, coordinated cross-functional delivery.

Built data-driven process models improving refinery throughput 8–10%; supported $50M+ capital investment decisions through scenario modeling and cost-benefit analysis.

• ▸Built data-driven process models improving refinery throughput 8-10%; supported $50M+ capital investment decisions through scenario modeling, cost-benefit analysis, and structured system analysis on high-stakes infrastructure projects.