# Senior Data Science Manager, Marketing

**Company:** [Reddit](https://hotfix.jobs/companies/reddit)
**Location:** Remote
**Role:** Data Analytics
**Salary:** $233k – $326k/yr
**Experience:** 5+ years
**Skills:** SQL, Python, R, Causal Inference, A/B Testing, Ltv Modeling, ETL, Experimentation, Metrics Governance, LLMs
**Posted:** 2026-04-22

> Leads a data science team driving marketing strategy through causal insights, LTV modeling, and measurement frameworks. Partners with product/marketing to enable data-driven decisions and self-serve analytics; requires 5+ years managing data scientists, SQL/Python proficiency.

## Job Description

## Responsibilities
- Lead and mentor a high-performing team of data scientists focused on Marketing & Business Development.
- Serve as thought-partner to cross-functional leaders (Product, Engineering, Design, Marketing), shaping roadmap and strategy with actionable insights.
- Lead initiatives to quantify incremental lift of marketing spend, develop/deploy Incremental/Predictive LTV models.
- Oversee establishment of measurement frameworks including experimental designs and causal inference methods for Brand Initiatives, campaign quality, and ROI.
- Drive adoption of strategic/tactical recommendations based on product experience and data understanding.
- Ensure data quality, oversee ETLs, reporting dashboards, data aggregations for business tracking/ML models.
- Lead initiatives to enable analytics self-serve capabilities for cross-functional teams.

## Required Qualifications
- Experience driving product strategy/roadmaps through analytics, ideally for consumer tech products/marketplaces.
- 5+ years leadership as people manager, leading teams of up to 10 data scientists with expertise in causal inference, A/B testing, metric definition/governance, product strategy.
- Led initiatives for analytics self-serve.
- Excel at communicating to technical/non-technical audiences.
- Ability to understand/explain complex concepts simply.
- Proficient in **SQL** and **Python/R** for hands-on analysis.
- Experience with AI-assisted tools (e.g., Cursor, Claude, LLMs) for productivity/code quality.
- Collaborative attitude, curiosity, evidence-backed product opinions.

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