Responsibilities
- Customer Value Infrastructure (Prove ROI at Every Level) Build the metrics that quantify customer-specific business impact: Design and maintain a real-time Customer ROI Engine calculating cost-per-successful-verification, fraud savings, conversion lift, and time-to-value by customer, segment, and use case Create customer-facing Value Dashboards showing verification success rates vs. industry benchmarks, cost efficiency trends, and projected savings Develop attribution models connecting verification outcomes to downstream business metrics (account activations, transaction completion, fraud prevented) Establish pricing intelligence at the customer level: Build granular unit economics visibility: cost-to-serve, margin contribution, and channel mix efficiency per customer Model willingness-to-pay signals and usage patterns to inform tiered pricing and custom packaging Quantify the revenue impact of workflow configurations (Silent Auth-first vs. SMS fallback economics)
- Channel Performance & Optimization (Make Every Verification Smarter) Create a single source of truth for channel economics: Unified performance metrics across SMS, Voice, Email, WhatsApp, and Silent Authentication: deliverability, latency, conversion rate, cost-per-success, and failure taxonomy Country × carrier × channel performance matrices with confidence intervals and anomaly flags Real-time channel health monitoring with automated alerting for degradation Build the intelligence layer for workflow optimization: Predictive models for optimal channel routing (next-best-channel given geography, time, customer segment, historical performance) Fallback effectiveness analysis: quantify conversion recovery and cost trade-offs for each fallback path Silent Authentication signal analysis: success/rejection drivers, speed benchmarks, and UX impact measurement
- Product Data Platform (Foundation for Autonomy) Design data architecture that enables autonomous decision-making: Define the canonical event schema and taxonomy for all verification touchpoints (API calls, webhook events, workflow steps, outcomes) Build certified, versioned datasets powering self-serve analytics, ML models, and customer-facing products Implement data quality infrastructure: lineage tracking, anomaly detection, freshness SLAs, and automated reconciliation Ship ML/analytics products that move toward autonomous verification: Conversion propensity models: predict verification success probability in real-time to optimize routing Fraud & abuse detection: anomaly scoring for traffic pumping, IRSF patterns, and bot behavior—with automated response recommendations Time-to-verify prediction: forecast completion time to enable SLA commitments and dynamic timeout tuning Customer segmentation: behavioral and commercial clustering for personalized workflows and pricing
- Monetization (Turn Data into Revenue) Develop data products that customers will pay for: Verification Intelligence Suite: premium analytics, industry benchmarks, and deliverability diagnostics Workflow Optimizer: ML-driven recommendations for channel sequencing, timeout configuration, and fallback strategies by geography and vertical Fraud Protection Package: risk scoring, pumping detection, and abuse pattern alerts with quantified savings Define commercial success: Package entitlements, usage thresholds, and upgrade triggers Track attach rates, retention lift, and expansion revenue attributable to data products Build the business case for each offering with clear ROI narratives
Requirements
- 5–8+ years in data science/analytics
- ≥2 years building and shipping data products
- Track record of translating ambiguous business questions into measurable outcomes
- Demonstrated ability to influence product and pricing decisions with data
Nice to Have
- Experience in B2B SaaS, identity/auth, fintech, messaging/telecom, or fraud analytics
