Senior Director, Analytics and Data Science, Core Product & Growth
New
J
JobgetherConsumer Technology
Remote-first working environment, allowing employees to work remotely within the US and CanadaFull-TimeDirector
SalaryUS base salary range of $247,000–$366,000 USD
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Job Details
- Experience
- 10+ years of experience in analytics, data science, decision science, or a closely related discipline, including at least 5 years in people management at a senior leadership level.
- Required Skills
- PythonSQLMachine LearningTableauData scienceDatabricksR
Requirements
- 10+ years of experience in analytics, data science, decision science, or a closely related discipline.
- At least 5 years in people management at a senior leadership level.
- Deep expertise in inferential and causal analytics.
- Strong knowledge of engagement, retention, churn, cohort analysis, and other consumer growth metrics.
- Proven experience forecasting a company-level business metric.
- Technical proficiency with SQL and Python or R.
- Experience with Databricks, experimentation platforms such as Statsig, Tableau or comparable BI tools.
- Experience with AI-native analytics tools such as Hex or AI/BI.
- Hands-on experience using coding agents such as Claude Code or equivalent tools.
- Bachelor's degree or equivalent practical experience.
- Demonstrated success transforming analytics teams into embedded, proactive strategic partners.
Responsibilities
- Lead and develop decision scientists and data scientists, establishing a high quality bar and coaching team members toward greater technical expertise and leadership.
- Own the models and forecasts used to understand and predict monthly active user growth, including key drivers behind acquisition, activation, engagement, retention, and churn.
- Lead exploratory and strategic analytics that identify opportunities to grow the user base and translate behavioral insights into actionable product and business priorities.
- Establish consistent standards for how analytical work is scoped, conducted, reviewed, communicated, and documented.
- Partner with Revenue Analytics and Data Engineering leadership to define common metrics, experimentation standards, and shared operating practices.
- Build an operating model that enables the analytics and data science organization to engage earlier in product and engineering roadmaps.
- Apply AI-native approaches to analytical work, including coding agents and automated analysis while defining which activities should be automated.
- Provide senior leadership with clear, evidence-based recommendations and determine which analytical questions deserve investment.
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