• We’re hiring a Director, Marketing Analytics to lead Offline and Brand measurement on our Marketing Science team
• You will own the Offline and Brand Measurement framework end-to-end: TV (linear and streaming), podcast, out-of-home, direct mail, and brand marketing
• That means ensuring our Marketing Mix Model is accurate, well-calibrated, and actually driving decisions for offline and brand spend
• You will design and execute incrementality tests and translate measurement outputs into concrete choices about offline budget allocation and channel strategy
• This is not a coordination role. It’s a hands-on, technically demanding position that requires you to be the expert in the room on offline and brand analytics, with a strong point of view on what’s working and what isn’t
• You’ll bring marketing measurement expertise to executive conversations: not just presenting data, but shaping how leadership thinks about offline and brand investment
• You will work closely with marketing leadership, media buyers, and the broader Marketing Science team
• Your output will directly shape how we invest across offline and brand channels
• This role reports to the Senior Director, Business Analytics
• Own the measurement strategy for TV (linear and CTV/streaming), podcast, OOH, direct mail, and brand marketing channels, including methodology selection, model governance, and insight delivery
• Own the accuracy and leverage of our Marketing Mix Model (MMM) for offline and brand spend. Ensure inputs are clean and representative, outputs are properly calibrated against incrementality ground truth, and results are translated into concrete budget allocation decisions rather than sitting in a report
• Own the team-wide incrementality roadmap and methodology standards. Design and execute incrementality experiments for offline and brand channels (geo holdouts, synthetic control, matched market tests) to establish causal impact and validate MMM outputs against ground truth
• Coordinate with paid digital analysts on test design and scheduling to avoid test collisions and maintain consistent methodology across online and offline
• Develop and own brand marketing measurement: brand lift studies, awareness-to-conversion funnel tracking, and long-run brand equity modeling
• Own podcast measurement, including attribution methodologies (vanity URLs, promo codes, pixel-based), reach estimation, and incrementality validation
• Translate MMM outputs and incrementality results into offline and brand budget allocation recommendations with clear confidence bounds. Decision-ready, not just slide-ready
• Develop and maintain channel-level media response curves, saturation analysis, and diminishing returns modeling for offline media to support scenario planning and spend optimization
• Partner with media buyers and marketing leadership to scope offline and brand measurement roadmaps, pressure-test interpretations, and ensure findings actually change how we invest
• Own and present a rolling incrementality roadmap to marketing leadership on a monthly cadence: what’s been tested, what results mean for spend, and what’s next. This is a recurring executive deliverable, not a one-off
• Dive deeply into offline and brand channel performance problems. Diagnose what’s working, what isn’t, and why. Deliver clear, actionable optimization recommendations that improve spend efficiency and channel effectiveness
• Identify data quality and tracking gaps specific to offline channels and drive the fixes, from S3 data ingestion (Tatari and similar) to upstream tagging and pipeline integrity
• Build scalable, reusable dbt models that encode offline and brand channel measurement logic for use across dashboards, reporting, and stakeholder self-serve
• Work in Snowflake, Python, and our Databricks environment to build and maintain the data pipelines and models that underpin offline and brand measurement
• Build and maintain Tableau dashboards that give marketing leadership a clear, reliable view of offline and brand channel performance and ROI
• Use AI fluently as a daily productivity layer: accelerating SQL, automating documentation, stress-testing model assumptions, and finding faster paths to answers
• Contribute to how the Marketing Science team builds and shares AI-assisted analytical workflows, not just as a user but as a practitioner who helps raise the floor

Benefits

• Medical, dental, vision and FSAs
• Unlimited, flexible time off
• Generous employer 401(k) match and equity
• 16-week paid parental leave
• Ongoing learning and development
• Remote-friendly work environment

• 8+ years of experience in marketing analytics or marketing science, with the majority of that time focused on offline and brand channels: TV (linear and/or streaming), podcast, radio, OOH, direct mail, or similar. You should have seen these measurement problems at a meaningful scale
• Comfortable operating without a manager-level data layer between you and the problem. You scope your own work, define your own roadmap, and drive to a conclusion without a lot of hand-holding
• Proven incrementality testing experience for offline channels: geo holdouts, matched market design, synthetic control, difference-in-differences. You know how to design a test with appropriate power and interpret results honestly
• Deep, hands-on Marketing Mix Modeling experience for offline and brand spend. You understand how MMMs are built, what the inputs require, where they break down, and how to validate outputs against incrementality ground truth
• Experience with TV measurement platforms and data providers: Tatari, iSpot, VideoAmp, Nielsen, or similar
• Strong SQL and data modeling skills. You write clean, efficient queries across complex schemas and can build analytics logic that others can maintain. dbt experience is a strong plus
• Python proficiency for analytical work: statistical modeling, data manipulation, and automation. You write readable code that a teammate can pick up
• Strong communicator. You have presented offline and brand measurement findings and budget recommendations to CMO-level stakeholders and know how to land technical results as decisions, not just data
• Not sure this one’s for you? Don’t count yourself out. Show us what you’ve got and we’ll reach out if there’s a great fit
• Familiarity with Bayesian modeling frameworks (PyMC, Stan, TFP) and how they underpin modern MMM approaches
• Prior work with podcast measurement methodologies and attribution vendors (e.g., Podsights, Chartable, host-read promo code tracking)
• Experience at a direct-to-consumer or fintech company where offline media is used to drive app registrations or account openings