Manager, Data Science

Overview

Lead a team of marketing data scientists in defining and executing end‑to‑end data science roadmaps that support lifecycle marketing across Quick Books Services (Capital, Payroll, Payments, Bill Pay) and Mailchimp, aligned to short‑and‑long‑term business outcomes.

Own the measurement, experimentation, and modeling strategy for lifecycle marketing initiatives, including onboarding, attach, upsell, retention, and active use, leveraging causal inference, experimentation frameworks, and advanced analytics.

Design, evaluate, and scale incrementality measurement approaches, including randomized experiments, holdouts, and quasi‑experimental methods, to quantify the true impact of lifecycle marketing across Email, IPD, Push, SMS, Web, and cross‑channel programs.

Drive development and adoption of machine learning models for lifecycle marketing use cases such as propensity scoring, churn risk, personalization, and next‑best‑action, in partnership with central DS and ML platform teams.

Translate complex analytical findings into clear, data‑backed perspectives on marketing and business performance, with actionable recommendations tied to customer growth, revenue, and retention.

Partner closely with Lifecycle Marketing, CRM Analytics, Product, GTM, and Finance to ensure strong metric definitions, data quality, and alignment between marketing performance and financial outcomes.

Shape forward‑looking data science capabilities by identifying gaps in experimentation, modeling, and data infrastructure, and influencing investments that improve learning velocity and decision‑making.

Manage a team of data scientists and contractors, including coaching on technical rigor, experimental design, modeling best practices, and data storytelling, while owning prioritization, intake, and delivery.

• Lead a team of marketing data scientists in defining and executing end‑to‑end data science roadmaps that support lifecycle marketing across Quick Books Services (Capital, Payroll, Payments, Bill Pay) and Mailchimp, aligned to short‑and‑long‑term business outcomes.
• Own the measurement, experimentation, and modeling strategy for lifecycle marketing initiatives, including onboarding, attach, upsell, retention, and active use, leveraging causal inference, experimentation frameworks, and advanced analytics.
• Design, evaluate, and scale incrementality measurement approaches, including randomized experiments, holdouts, and quasi‑experimental methods, to quantify the true impact of lifecycle marketing across Email, IPD, Push, SMS, Web, and cross‑channel programs.
• Drive development and adoption of machine learning models for lifecycle marketing use cases such as propensity scoring, churn risk, personalization, and next‑best‑action, in partnership with central DS and ML platform teams.
• Translate complex analytical findings into clear, data‑backed perspectives on marketing and business performance, with actionable recommendations tied to customer growth, revenue, and retention.
• Partner closely with Lifecycle Marketing, CRM Analytics, Product, GTM, and Finance to ensure strong metric definitions, data quality, and alignment between marketing performance and financial outcomes.
• Shape forward‑looking data science capabilities by identifying gaps in experimentation, modeling, and data infrastructure, and influencing investments that improve learning velocity and decision‑making.
• Manage a team of data scientists and contractors, including coaching on technical rigor, experimental design, modeling best practices, and data storytelling, while owning prioritization, intake, and delivery.

• 7+ years of experience applying data science, advanced analytics, or quantitative methods to marketing, growth, or lifecycle use cases.
• Experience leading and developing teams of data analysts or data scientists, with a demonstrated ability to coach both technical and business skills. Strong expertise in statistics, experimental design, and causal inference, including A/B testing, multivariate testing, and incremental lift measurement.
• Hands‑on experience building or operationalizing machine learning models (e.g., propensity, segmentation,…