Senior Data Scientist

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 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