Senior Data Scientist

<div class="content-intro"><h2><strong>Join Vonage and help us innovate cloud communications for businesses worldwide!</strong></h2></div><h1><strong>Senior Data Scientist — Verify V2 Data Products, Insights &amp; Monetization</strong></h1>
<h2><strong>Mission</strong></h2>
<p>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.</p>
<p>You'll own the end-to-end data pipeline from raw events to customer-visible metrics that answer the question every customer asks: <em>"What is this product actually worth to my business?"</em></p>
<h2><strong>What You'll Own</strong></h2>
<h3><strong>1. Customer Value Infrastructure (Prove ROI at Every Level)</strong></h3>
<p><strong>Build the metrics that quantify customer-specific business impact:</strong></p>
<ul>
<li>Design and maintain a real-time <strong>Customer ROI Engine</strong> calculating cost-per-successful-verification, fraud savings, conversion lift, and time-to-value by customer, segment, and use case</li>
<li>Create customer-facing <strong>Value Dashboards</strong> showing verification success rates vs. industry benchmarks, cost efficiency trends, and projected savings</li>
<li>Develop <strong>attribution models</strong> connecting verification outcomes to downstream business metrics (account activations, transaction completion, fraud prevented)</li>
</ul>
<p><strong>Establish pricing intelligence at the customer level:</strong></p>
<ul>
<li>Build granular unit economics visibility: cost-to-serve, margin contribution, and channel mix efficiency per customer</li>
<li>Model willingness-to-pay signals and usage patterns to inform tiered pricing and custom packaging</li>
<li>Quantify the revenue impact of workflow configurations (Silent Auth-first vs. SMS fallback economics)</li>
</ul>
<h3><strong>2. Channel Performance &amp; Optimization (Make Every Verification Smarter)</strong></h3>
<p><strong>Create a single source of truth for channel economics:</strong></p>
<ul>
<li>Unified performance metrics across SMS, Voice, Email, WhatsApp, and Silent Authentication: deliverability, latency, conversion rate, cost-per-success, and failure taxonomy</li>
<li>Country × carrier × channel performance matrices with confidence intervals and anomaly flags</li>
<li>Real-time channel health monitoring with automated alerting for degradation</li>
</ul>
<p><strong>Build the intelligence layer for workflow optimization:</strong></p>
<ul>
<li>Predictive models for optimal channel routing (next-best-channel given geography, time, customer segment, historical performance)</li>
<li>Fallback effectiveness analysis: quantify conversion recovery and cost trade-offs for each fallback path</li>
<li>Silent Authentication signal analysis: success/rejection drivers, speed benchmarks, and UX impact measurement</li>
</ul>
<h3><strong>3. Product Data Platform (Foundation for Autonomy)</strong></h3>
<p><strong>Design data architecture that enables autonomous decision-making:</strong></p>
<ul>
<li>Define the canonical event schema and taxonomy for all verification touchpoints (API calls, webhook events, workflow steps, outcomes)</li>
<li>Build certified, versioned datasets powering self-serve analytics, ML models, and customer-facing products</li>
<li>Implement data quality infrastructure: lineage tracking, anomaly detection, freshness SLAs, and automated reconciliation</li>
</ul>
<p><strong>Ship ML/analytics products that move toward autonomous verification:</strong></p>
<ul>
<li><strong>Conversion propensity models</strong>: predict verification success probability in real-time to optimize routing</li>
<li><strong>Fraud &amp; abuse detection</strong>: anomaly scoring for traffic pumping, IRSF patterns, and bot behavior—with automated response recommendations</li>
<li><strong>Time-to-verify prediction</strong>: forecast completion time to enable SLA commitments and dynamic timeout tuning</li>
<li><strong>Customer segmentation</strong>: behavioral and commercial clustering for personalized workflows and pricing</li>
</ul>
<h3><strong>4. Monetization (Turn Data into Revenue)</strong></h3>
<p><strong>Develop data products that customers will pay for:</strong></p>
<ul>
<li><strong>Verification Intelligence Suite</strong>: premium analytics, industry benchmarks, and deliverability diagnostics</li>
<li><strong>Workflow Optimizer</strong>: ML-driven recommendations for channel sequencing, timeout configuration, and fallback strategies by geography and vertical</li>
<li><strong>Fraud Protection Package</strong>: risk scoring, pumping detection, and abuse pattern alerts with quantified savings</li>
</ul>
<p><strong>Define commercial success:</strong></p>
<ul>
<li>Package entitlements, usage thresholds, and upgrade triggers</li>
<li>Track attach rates, retention lift, and expansion revenue attributable to data products</li>
<li>Build the business case for each offering with clear ROI narratives</li>
</ul>
<h2><strong>Key Responsibilities</strong></h2>
<ul>
<li><strong>Own the customer value narrative</strong>: Build and maintain the infrastructure that lets every customer (and our sales team) articulate Verify's ROI in dollars and percentages</li>
<li><strong>Ship production ML systems</strong>: From feature engineering through deployment, monitoring, and iteration</li>
<li><strong>Create reliable, self-serve data products</strong>: Dashboards, APIs, and datasets that scale without manual intervention</li>
<li><strong>Drive pricing and packaging decisions</strong>: Provide the quantitative foundation for how we charge and what we bundle</li>
<li><strong>Partner across the organization</strong>: Work with Product, Engineering, Finance, Sales, and Customer Success to embed data into every decision</li>
<li><strong>Report to leadership</strong>: Own KPI narratives on margin drivers, growth levers, and competitive positioning</li>
</ul>
<h2><strong>Success Measures</strong></h2>
<table>
<tbody>
<tr>
<td>
<p><strong>Area</strong></p>
</td>
<td>
<p><strong>Target KPIs</strong></p>
</td>
</tr>
<tr>
<td>
<p><strong>Customer Value Proof</strong></p>
</td>
<td>
<p>100% of enterprise customers have ROI dashboards; X% increase in documented customer savings</p>
</td>
</tr>
<tr>
<td>
<p><strong>Channel Optimization</strong></p>
</td>
<td>
<p>+X% conversion rate improvement; −X seconds median time-to-verify; −X% cost-per-success</p>
</td>
</tr>
<tr>
<td>
<p><strong>Fraud &amp; Abuse</strong></p>
</td>
<td>
<p>−X% fraudulent traffic; $Xm in prevented losses; <X% false positive rate</p>
</td>
</tr>
<tr>
<td>
<p><strong>Data Product Revenue</strong></p>
</td>
<td>
<p>X% attach rate on premium insights; $Xm incremental ARR from data products</p>
</td>
</tr>
<tr>
<td>
<p><strong>Platform Readiness</strong></p>
</td>
<td>
<p>Certified datasets powering ≥3 autonomous routing decisions; <Xms model inference latency</p>
</td>
</tr>
</tbody>
</table>
<h2><strong>What "Great" Looks Like</strong></h2>
<h3><strong>Core Data Science</strong></h3>
<ul>
<li>Experimentation design and causal inference (A/B testing, CUPED, uplift modeling, instrumental variables)</li>
<li>Predictive modeling: classification, survival analysis, time series, real-time scoring</li>
<li>Anomaly detection with adversarial thinking (fraud patterns, traffic manipulation, abuse signals)</li>
<li>Customer analytics: segmentation, LTV modeling, churn prediction, cohort economics</li>
</ul>
<h3><strong>Data Engineering Fluency</strong></h3>
<ul>
<li>Strong SQL; Python (pandas, scikit-learn, PySpark); comfortable shipping production code</li>
<li>Event-driven architecture: streaming pipelines and real-time analysis and adaptation (Apache Flink), webhook processing, idempotency, late-arrival handling</li>
<li>Data modeling: star schemas, semantic layers, data contracts, metric certification</li>
<li>MLOps: feature stores, model monitoring, CI/CD for analytics, orchestration (Airflow/Dagster)</li>
</ul>
<h3><strong>Product &amp; Commercial Analytics</strong></h3>
<ul>
<li>Pricing analytics: unit economics, willingness-to-pay estimation, margin optimization</li>
<li>Funnel analysis for multi-step, multi-channel workflows</li>
<li>Dashboard design and narrative clarity (Looker, Tableau, dbt metrics layer)</li>
<li>Packaging and monetization strategy for data products</li>
</ul>
<h3><strong>Domain Expertise (Highly Valued)</strong></h3>
<ul>
<li>CPaaS, verification, or 2FA: OTP mechanics, deliverability constraints, carrier relationships</li>
<li>Silent Authentication: network-based verification, success/rejection drivers, integration patterns</li>
<li>Fraud and risk: traffic pumping, IRSF, bot detection, abuse economics</li>
<li>Privacy and compliance: GDPR/CCPA, data minimization, audit requirements, customer-facing data controls</li>
</ul>
<h2><strong>Background</strong></h2>
<ul>
<li>5–8+ years in data science/analytics, with ≥2 years building and shipping data products</li>
<li>Track record of translating ambiguous business questions into measurable outcomes</li>
<li>Experience in B2B SaaS, identity/auth, fintech, messaging/telecom, or fraud analytics preferred</li>
<li>Demonstrated ability to influence product and pricing decisions with data</li>
</ul>
<h2><strong>Why This Role Matters</strong></h2>
<p>Verification is shifting from a cost center to a strategic differentiator. The data infrastructure you build will:</p>
<ol>
<li><strong>Prove value</strong> — Give every customer undeniable evidence of ROI</li>
<li><strong>Optimize economics</strong> — Make every verification faster, cheaper, and more reliable</li>
<li><strong>Enable autonomy</strong> — Lay the foundation for a platform that routes, optimizes, and protects without human intervention</li>
</ol>
<p>You'll shape how Vonage—and our customers—think about iden

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