Data Scientist

Job Description:

Support end-to-end campaign audience build, measurement, and reporting for prioritized Growth Marketing campaigns translating marketing objectives into audience specifications, measurement requirements, and dashboards that campaign owners can act on.

• Campaign audience builds translate eligibility and targeting criteria into audience specs, size audiences, validate against holdout and targeting logic, and re-validate after platform changes.
• Measurement & reporting requirements define campaign signals, KPIs, and reporting specifications per campaign; feasibility-check proposed solutions with stakeholders before build.
• Dashboards & QA build and maintain campaign dashboard suites (operational visibility plus quarterly performance reporting), monitoring and alerting, and automated QA frameworks; update pipelines when data sources change.
• Campaign triage identify measurement and reporting gaps, drive resolution with Mar Tech and Data Engineering, and document outcomes.
• Insights & readouts ad hoc analyses, performance deep-dives, and quarterly reporting packages.
• Documentation keep runbooks, troubleshooting guides, and the team knowledge repository current.
• Automation & AI-assisted workflows identify repeat manual steps in campaign build and reporting and automate them, including AI/agent-based analytical workflows where appropriate.

• experience in analytics or data science marketing analytics, growth/campaign analytics, CRM analytics, product analytics, or a quantitative data science role.
• Degree or equivalent practical background in a quantitative field (statistics, economics, mathematics, computer science, engineering, or similar).
• Advanced SQL able to write performant queries against large, messy datasets and build reproducible, well-documented logic.
• Python (or R) for analysis and automation: data wrangling, scripting, and repeatable reporting.
• Applied statistics experiment design, A/B testing, significance and power, incrementality and holdout measurement, and the judgment to know when a result is not real.
• Hands‑on dashboarding and data modeling; comfortable owning a pipeline or dashboard end‑to‑end, not only consuming one.
• Demonstrated ability to take an ambiguous marketing question, scope it into an analytical plan, and deliver a recommendation non‑technical stakeholders can act on.
• Works autonomously across multiple concurrent campaign timelines with limited direction.

• AI/ML experience practical use of LLMs or agentic tooling to accelerate analysis, reporting, or QA (prompt design, agent workflows, evaluating output quality), and/or applied ML such as propensity and lifetime-value modeling, segmentation, uplift modeling, or forecasting.
• Causal inference beyond basic A/B testing difference-in-differences, geo experiments, synthetic control, matching.
• Marketing automation / martech platform exposure Salesforce-type marketing clouds, email and messaging channels, audience management tools.
• Lifecycle or CRM marketing measurement at scale; familiarity with funnel metrics (CTR, conversion, adoption, retention).
• Data engineering adjacency dbt-style modeling, orchestration, pipeline reliability, and data quality tooling.
• SMB or B2B marketing background.

Pursuant to the California Fair Chance Act, Los Angeles County Fair Chance Ordinance for Employers, Los Angeles Fair Chance Initiative for Hiring Ordinance, and San Francisco Fair Chance Ordinance, qualified applicants will be considered for assignment with records. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to…