Data Science Manager, GTM

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

89% 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

Data Science Manager, GTM San Francisco About the Role: The Manager of Data Science for GTM is a hands-on player coach who leads from the front across Sales, Customer Success, and Marketing analytics. They are deeply technical, opinionated about the GTM analytical agenda, and accountable for both the output of the team and the development of each person on it. They are not a pure people manager - they roll up their sleeves on the hardest problems, set the standard for what good looks like, and pull the team up through hands-on coaching, technical mentorship, and direct involvement in the work. They own the analytical strategy for the GTM function and are a credible, trusted partner to Sales, CS, and Marketing leadership. What You'll Do: - Set the analytical strategy and roadmap for GTM, proactively identifying the highest-leverage opportunities before they are asked, across the full customer lifecycle from acquisition through expansion and retention. - Operate as a player coach, stay technical, get into the weeds on the hardest GTM problems, and model the analytical rigor and DS thinking you expect from the team. - Coach the team directly on the work itself: review analyses, push on methodology, challenge correlation versus causation, and raise the bar for statistical depth, EDA, and storytelling. - Own the measurement strategy for GTM, defining how we evaluate the true impact of marketing campaigns, sales motions, and CS interventions through rigorous causal methods, not just attribution heuristics. - Drive development and iteration of churn risk,

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