Senior Data Scientist — Verify V2 Data Products, Insights & Monetization

Mission

Build the quantitative foundation that proves and amplifies Verify v2's value—transforming verification telemetry into a reliable, customer-facing data infrastructure that demonstrates measurable ROI, optimizes channel economics, and lays the groundwork for an autonomous identity and verification platform.

You'll own the end-to-end data pipeline from raw events to customer-visible metrics that answer the question every customer asks: "What is this product actually worth to my business?"

What You'll Own

1. 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)

2. 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

3. 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 personalize