Principal Marketing Data Scientist, Quizlet

• 15+ years in marketing analytics/data science (consumer tech), including principal-level individual contributor impact; depth in TOF growth
• Hands-on leadership of incrementality testing (geo/user holdouts, heavy-ups, synthetic controls) and MMM programs; fluency in limits of MTA and calibration across methods
• Proven budget allocation work: response curves, scenario planning, and payback/LTV modeling that drove quarterly investment decisions
• Advanced SQL and Python or R (pandas/NumPy; Bayesian modeling with PyMC/Stan a plus)
• Production analytics in BigQuery/Snowflake/Redshift with dbt modeling and orchestration (Airflow/Dagster)
• Executive-grade storytelling via Looker/Tableau/Mode; KPI design that ladders to business outcomes
• Practical literacy with GA4, tag managers, MMPs, ad platforms (search/social/video/display/CTV), CDP, and survey/brand-lift tools
• Demonstrated ability to separate seasonality from media effects and to turn noisy platform signals into causally sound recommendations

Desirable

• This role sits at the intersection of AI & Data and Marketing, building the measurement and insights that unlock dramatic top-of-funnel growth
• We’re hiring a Principal Marketing Data Scientist to be the technical and strategic engine behind TOF growth—designing trusted measurement, separating correlation from causation, and turning models into concrete decisions on budget, channels, audiences, and creative
• You won’t manage people; you’ll lead through expertise, partnering closely with marketing data science, data engineering, martech, and channel owners to deliver repeatable, compounding impact
• Own TOF measurement & incrementality
• Design an always-on cross-channel measurement architecture (paid, organic/SEO, owned, influencer/affiliate, brand, CTV/offline)
• Stand up geo/user holdouts, heavy-ups, synthetic controls, CUPED/diff-in-diff, and brand-lift/survey programs; produce trustworthy incremental reach/qualified visit estimates
• Triangulate MMM + incrementality + platform MTA with decision rules (MMM for budget setting, holdouts for validation, platform data for operations)
• Forecast, allocate, and de-risk spend: build response curves from media → reach → qualified visits → sign-ups → LTV with uncertainty bounds
• Deliver marginal dollar recommendations by channel/geo/audience
• Model payback windows (30-day to 3-year LTV) across offer/price/creative scenarios; define guardrails for frequency, saturation, and diminishing returns
• Ship reporting that drives decisions (not just dashboards)
• Build exec-ready views that connect leading indicators (brand search, share of voice, qualified-visit rate, creative score) to lagging outcomes (sign-ups, retention, LTV)
• Run always-on traffic-source health with red-flag monitoring and forward projections; quantify the cost to hit growth targets under uncertainty
• Unlock audience & creative leverage by creating a creative learning agenda and taxonomy; analyze fatigue, hooks, and motifs; document lift and roll into playbooks
• Drive cohort and growth-segment insights by acquisition date/platform/geo/demo/channel to inform media, landing experiences, and lifecycle sequencing
• Raise the bar on tooling, data quality, and privacy
• Specify and evaluate the measurement stack (analytics/MMP, incrementality/MMM, survey/brand tracking, CDP/tagging); prioritize buy-vs-build
• Define event/identity standards and data quality SLAs across web/app; navigate ATT and Privacy Sandbox constraints with first-party data
• Lead through influence: coach peers, set analysis and code standards, and create documentation that makes insights reusable
• Translate ambiguity (“grow awareness”) into hypotheses, experiments, and clear trade-off recommendations for senior leaders