Senior Data Scientist at Vonage

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; <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: • Prove value — Give every customer undeniable evidence of ROI • Optimize economics — Make every verification faster, cheaper, and more reliable • 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. #LI-WW1 There’s no perfect candidate. You don't need all the preferred qualifications to make a valuable impact on our team. Our employees and customers come from diverse backgrounds, so if you're passionate about what you could achieve at Vonage, we'd love to hear from you. To learn how we process your personal data during the recruitment process, please refer to our Privacy Notice. Who we are: Vonage is a global cloud communications leader. And your talent will further help brands - such as Airbnb, Viber, WhatsApp, and Snapchat - accelerate their digital transformation through our fully programmable-based unified communications, contact center solutions, and communications APIs. Ready to innovate? Then join us today. Note: The purpose of this profile is to provide a general summary of essential responsibilities for the position and is not meant as an exhaustive list. Assignments may differ for individuals within the same role based on business conditions, departmental need or geographic location.