Marketing Data Scientist

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

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Salary

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401(k) match

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Market context

Median wage (BLS OEWS)

$111,944 national median

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

52% above the BLS national median for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Marketing Data Scientist Remote, US About the Role: The Marketing Data Scientist is a rigorous, intellectually curious problem solver embedded directly within the Marketing function for the GTM team. They own the science behind how we understand, attract, and grow our customers - building the models, measurement frameworks, and experiments that turn complex marketing data into decisions that move the business forward. They do not just report on what happened - they design the studies that tell us why and build the models that tell us what comes next. What You'll Do: - Frame ambiguous marketing problems as well-scoped modeling and measurement challenges and own them end to end. - Develop and own attribution models that accurately allocate marketing investment across channels and touchpoints. - Build propensity, lead scoring, and churn models to prioritize where GTM teams should focus. - Model the relationship between product engagement signals and downstream commercial outcomes - expansion, retention, and conversion. - Design, execute, and interpret experiments (A/B, MVT, geo-based) with appropriate power analysis and statistical validity. - Build and apply segmentation models and cohort analyses to uncover behavioral patterns, lifecycle trends, and funnel opportunities. - Analyze organic search and AEO signals as modeling inputs to inform content strategy and improve discoverability. - Partner with analytics engineers to productionize models and move insights from notebook to pipeline. - Use AI tools to move faster, explore unfamiliar methods, and surface modeling options - while developing the judgment to know when AI-generated outputs are wrong or incomplete.

Read the full description at www.circleci.com. FewerJobs shows a source-linked preview and links to the original posting.