VP of Data Product

Attain•Redwood City, CA

About The Position

Attain is hiring a VP of Data Product to own the strategy, scientific integrity, and product trajectory of its data asset, including the production of feeds licensed by external clients and used by internal teams. The role sits above the Data Products execution function, which handles day-to-day feed delivery, pipeline operations, validation, and vendor relationships. The VP will decide what to build, why it is valuable, and how it should be commercialized, while ensuring that shipped products are scientifically defensible. The role owns three product families: telemetry of Attain’s consumer-permissioned transaction and survey data, from ingestion through transformation and downstream consumption; marketing science feeds built from observed transaction data at merchant and CPG product levels, including derived measures such as incrementality, purchase propensity, ad responsiveness, brand switching, and loyalty; and extrapolated or predicted data, including population-level projections, panel-to-population weighting, forecasted behavior, and predictive scores. The VP partners with data science teams on derived and predicted products, helping set priorities, evaluate methods and outputs, and make ship/no-ship decisions. The VP will lead Data Product PMs and the execution function, set a multi-year portfolio roadmap, translate market and client needs into product and modeling priorities, and work with Strategy on commercial delivery. The role also applies AI methods to the data asset, treats internal teams as customers, and partners with Compliance and Consumer App product leaders to ensure products honor consumer permissions and regulatory obligations. Success is measured by revenue growth, adoption and retention; documented and validated methodologies that withstand scrutiny; a reliable, high-functioning product organization and data science partnership; greater internal use of canonical feeds rather than one-off extracts; and external recognition of Attain’s data as a credible standard for consumer-permissioned purchase data.

Requirements

Extensive experience across data products, marketing science, ad measurement, or media analytics.
At least 3 years of experience leading teams.
At least one role owning a commercially licensed data product, such as feeds, scores, APIs, or datasets, with real revenue and SLAs.
Experience applying AI methods to data extrapolation or similar work.
Experience managing senior product leaders and technical execution functions; experience leading leaders rather than only individual contributors.
Working fluency in marketing science and causal measurement, including evaluating incrementality methods, identifying selection bias or flawed baselines, and engaging effectively with PhD-level scientists and skeptical client analysts.
Experience directing data scientists by setting priorities, reviewing approaches, and making ship/no-ship decisions on modeled outputs.
Deep understanding of transaction-level data products, merchant and UPC/SKU structures, taxonomy and categorization, identity resolution and match rates, and panel-to-population projection.
Fluency in how advertisers, agencies, and platforms use data for audience activation, measurement, planning, clean room workflows, and identity.
Track record converting data assets into business outcomes, including licensing revenue growth and client adoption.
Executive-level communication skills, including presenting portfolio strategy to senior leadership, methodology to client data science teams, and priorities to engineering.
Experience with consumer-permissioned, panel, or purchase graph data and its privacy, consent, and compliance considerations.
Exposure to retail media networks, CTV measurement, unified IDs, and walled-garden constraints.
Ability to engage with classical marketing science methods, including discrete choice, Dirichlet/NBD loyalty math, Markov switching, and MMM/attribution, as well as modern causal ML, with healthy skepticism about assumptions.

Nice To Haves

Hands-on modeling history.
Familiarity with data licensing, pricing strategy, and clean room delivery platforms or workflows such as Snowflake, LiveRamp, or Databricks.
Ad tech startup or scale-up experience and comfort operating hands-on when needed.

Responsibilities

Set data product strategy and define the multi-year portfolio roadmap across the underlying data asset, marketing science feeds, and extrapolated feeds.
Lead Data Product PMs and the execution function responsible for feed delivery, pipeline architecture, quality assurance, match tests, and vendor management; set standards and remove blockers.
Partner with the data science pod to translate client and market needs into a modeling agenda and model outputs into shippable, explainable products.
Ensure scientific defensibility and work with the Strategy counterpart to support commercial delivery.
Productize marketing science methods and measures, including movable-middle propensity scoring, incrementality measurement, Markov-based switching and retention metrics, and brand choice modeling, as licensed feeds and scores with defensible methodology explanations.
Apply AI models and methods to advance prediction and scale of the data asset.
Treat internal measurement, insights, audience, and activation teams as customers of the underlying data asset.
Partner with Compliance and Consumer App product leaders to uphold consumer permissions and regulatory obligations.
Drive data product revenue growth, client adoption and retention, supported by trusted quality and methodology.
Establish documented, validated methodologies for derived and predicted fields that withstand advertiser, agency, and academic scrutiny.
Build a scaled, high-functioning data product organization, reliable delivery operations, a productive data science partnership, and clear career paths.
Promote use of canonical feeds over one-off extracts and improve internal customer satisfaction.
Build external recognition of Attain’s data as a credible standard for consumer-permissioned purchase data.