Senior Data Scientist, Marketing

Carrum Health•,

2h•$190,000 - $225,000•Remote

About The Position

Carrum Health is seeking a Senior Data Scientist to serve as the dedicated data science partner for its Marketing team as campaign volume grows. The role is part of the Data team and works closely with marketing leadership and the Senior Manager, Insights, Measurement & Analytics. The Senior Data Scientist owns the rigor and reliability of marketing measurement, establishes what the data can support, and contributes a data perspective to priorities and trade-offs. The role establishes standards for campaign testing and decision-making, designs powered holdouts, applies causal inference when randomization is not feasible, and builds multi-touch attribution. It diagnoses changes in marketing metrics and develops monitoring to surface issues early, including verifying campaign sends against eligible populations. It also designs, trains, and deploys models for lifetime value, registration-to-episode drop-off, and population segmentation, and partners with the core Data Science team on propensity and personalization models. The role owns how models are applied in campaigns, including targeting and personalized content and timing across email and web. The Senior Data Scientist builds repeatable measurement and reusable data models in Carrum’s shared data mart and semantic layer, rather than relying on one-off analyses. They translate marketing questions into testable questions, communicate the limits of the evidence, and challenge conclusions that are not methodologically sound. Carrum is a remote-first company. The role may involve occasional travel for team offsites, client events, or company gatherings. Chicago and San Francisco offices are available as an option. The stated salary range is $190,000–$225,000 depending on geography and experience, plus equity and an annual bonus.

Requirements

Experience building the technical foundation for marketing or growth measurement and experimentation from 0 to 1, and using it to change campaign or spend decisions.
Experience designing holdouts and lift tests, estimating incrementality from observational data when randomization is not possible, and building multi-touch attribution with an understanding of its limitations.
Experience building and deploying marketing models such as LTV, propensity, churn, or segmentation, using frameworks such as scikit-learn, XGBoost, or LightGBM.
Experience monitoring models in production; examples named include MLflow and Airflow.
Fluency in marketing concepts and metrics such as CAC, LTV, ROI, conversion funnels, and campaign metrics, and how they connect to business outcomes.
Ability to investigate the causes of metric changes and build monitoring to catch issues in the future.
Production-quality Python and SQL skills, including shipping repeatable, tested work and contributing to shared data models and a semantic layer.
Ability to partner with non-technical stakeholders, provide a point of view, and push back constructively when needed.
Methodological rigor, including distinguishing correlation from causation, challenging results, and validating AI-assisted work.
Understanding of responsible handling of healthcare data, including minimum-necessary access to sensitive data.

Nice To Haves

Forecasting experience.
Experience with personalization and recommendation systems.
Experience with eligibility-based or benefits-style funnels.

Responsibilities

Establish design standards and decision rules for campaign tests, including designing adequately powered holdouts.
Use causal inference to assess campaign impact when randomized testing is not feasible, and build multi-touch attribution while accounting for its limitations.
Diagnose changes in marketing metrics and build monitoring to catch issues early, including checking campaign sends against the eligible population.
Design, train, and deploy models for lifetime value, registration-to-episode drop-off, and eligible-population segmentation.
Partner with the core Data Science team on propensity and personalization models, and own their use in campaign targeting, personalized content, and timing across email and web.
Build repeatable measurement and reusable data models in the shared data mart and semantic layer as campaign volume grows.
Translate marketing questions into testable questions, explain what the data can and cannot support, and challenge analyses that are not robust.
Contribute production-quality, tested Python and SQL work to shared data models and the semantic layer.
Partner with marketing stakeholders, provide a data perspective on priorities and trade-offs, and constructively push back when requests do not address the underlying problem.
Monitor deployed models and validate analytical results, including AI-assisted work.

Benefits

Stock option plan and performance bonus for eligible employees.
Flexible schedules and remote work.
Flexible PTO, within reason.
Summer Fridays, with sign-off at 1 p.m. local time.
15 or more paid holidays, including Juneteenth, World Mental Health Day, and Carrum Health Day.
Annual holiday shutdown in December.
Paid parental leave.
Health, vision, and dental insurance.
Employee Assistance Programs (EAP).
401(k) retirement plan.
Occasional opportunities to connect virtually and in person.
Chicago and San Francisco offices available as an option.