Senior Data Scientist

Sobre el puesto

Join Vonage and help us innovate cloud communications for businesses worldwide!

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;

Data Product Revenue

X% attach rate on premium insights; $Xm incremental ARR from data products

Platform Readiness

Certified datasets powering ≥3 autonomous routing decisions;

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…