About the company
Join Vonage and help us innovate cloud communications for businesses worldwide!
Responsibilities
- 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.
- Own the end-to-end data pipeline from raw events to customer-visible metrics.
- 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.
- Establish pricing intelligence at the customer level: granular unit economics, willingness-to-pay signals, and revenue impact of workflow configurations.
- Create a single source of truth for channel economics: unified performance metrics across SMS, Voice, Email, WhatsApp, and Silent Authentication.
- Build predictive models for optimal channel routing and fallback effectiveness analysis.
- Design data architecture that enables autonomous decision-making: canonical event schema, certified datasets, data quality infrastructure.
- Ship ML/analytics products: conversion propensity models, fraud & abuse detection, time-to-verify prediction, customer segmentation.
- Develop data products that customers will pay for: Verification Intelligence Suite, Workflow Optimizer, Fraud Protection Package.
- Define commercial success: package entitlements, usage thresholds, upgrade triggers, track attach rates, retention lift, and expansion revenue.
- Own KPI narratives on margin drivers, growth levers, and competitive positioning.
Requirements
- 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 pumping, IRSF).
- Strong programming skills (Python, SQL) and experience with data engineering pipelines.
- Experience shipping production ML systems from feature engineering through deployment and monitoring.
- Ability to create reliable, self-serve data products: dashboards, APIs, and datasets.
- Partner across Product, Engineering, Finance, Sales, and Customer Success.
Conditions
- Work from Home - Spain.
- Opportunity to work on impactful data products in a global technology company.