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 personalized workflows and pricing
4. 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
Key Responsibilities
- Own the customer value narrative: Build and maintain the infrastructure that lets every customer (and our sales team) articulate Verify's ROI in dollars and percentages
- Ship production ML systems: From feature engineering through deployment, monitoring, and iteration
- Create reliable, self-serve data products: Dashboards, APIs, and datasets that scale without manual intervention
- Drive pricing and packaging decisions: Provide the quantitative foundation for how we charge and what we bundle
- Partner across the organization: Work with Product, Engineering, Finance, Sales, and Customer Success to embed data into every decision
- Report to leadership: Own KPI narratives on margin drivers, growth levers, and competitive positioning
Success Measures
Customer Value Proof
- 100% of enterprise customers have ROI dashboards; X% increase in documented customer savings
Channel Optimization
- +X% conversion rate improvement; −X seconds median time-to-verify; −X% cost-per-success
Fraud & Abuse
- −X% fraudulent traffic; $Xm in prevented losses; <X% false positive rate
Data Product Revenue
- X% attach rate on premium insights; $Xm incremental ARR from data products
Platform Readiness
- Certified datasets powering ≥3 autonomous routing decisions; <Xms model inference latency
What "Great" Looks Like
Core Data Science
- Experimentation design and causal inference (A/B testing, CUPED, uplift modeling, instrumental variables)
- Predictive modeling: classification, survival analysis, time series, real-time scoring
- Anomaly detection with adversarial thinking (fraud patterns, traffic manipulation, abuse signals)
- Customer analytics: segmentation, LTV modeling, churn prediction, cohort economics
Data Engineering Fluency
- Strong SQL; Python (pandas, scikit-learn, PySpark); comfortable shipping production code
- Event-driven architecture: streaming pipelines and real-time analysis and adaptation (Apache Flink), webhook processing, idempotency, late-arrival handling
- Data modeling: star schemas, semantic layers, data contracts, metric certification
- MLOps: feature stores, model monitoring, CI/CD for analytics, orchestration (Airflow/Dagster)
Product & Commercial Analytics
- Pricing analytics: unit economics, willingness-to-pay estimation, margin optimization
- Funnel analysis for multi-step, multi-channel workflows
- Dashboard design and narrative clarity (Looker, Tableau, dbt metrics layer)
- Packaging and monetization strategy for data products
Domain Expertise (Highly Valued)
- CPaaS, verification, or 2FA: OTP mechanics, deliverability constraints, carrier relationships
- Silent Authentication: network-based verification, success/rejection drivers, integration patterns
- Fraud and risk: traffic pumping, IRSF, bot detection, abuse economics
- Privacy and compliance: GDPR/CCPA, data minimization, audit requirements, customer-facing data controls
Background
- 5–8+ years in data science/analytics, with ≥2 years building and shipping data products
- Track record of translating ambiguous business questions into measurable outcomes