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 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

Area	Target KPIs
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
• Experience in B2B SaaS, identity/auth, fintech, messaging/telecom, or fraud analytics preferred
• Demonstrated ability to influence product and pricing decisions with data

Why This Role Matters

Verification is shifting from a cost center to a strategic differentiator. The data infrastructure you build will:

1. Prove value — Give every customer undeniable evidence of ROI
2. Optimize economics — Make every verification faster, cheaper, and more reliable
3. Enable autonomy — Lay the foundation for a platform that routes, optimizes, and protects without human intervention

You'll shape how Vonage—and our customers—think about identity verification as a measurable, optimizable, intelligent system.

 

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