VP of Data Product

Attain•Chicago, NY

About The Position

About Attain Built for consumers and companies, alike In a world driven by data, we believe consumers and businesses can coexist. Our founders had a vision to empower consumers to leverage their greatest asset—their data—in exchange for modern financial services. Built with this vision in mind, our platform allows consumers to access savings tools, earned wages and rewards without cost or hidden fees. In exchange, they give permission to use their real-time data for research, insights and targeted advertising. At Attain, your contribution will help us build a more equitable and efficient data sharing ecosystem—whether helping consumers access modern financial services or businesses leverage data to achieve better outcomes. You’ll have the opportunity to work directly with hands-on leaders and mission-driven individuals everyday. About the Role We're hiring a VP of Data Product to own our data as a business: the strategy, scientific integrity, and product trajectory of the underlying data asset and productionalizing feeds that external clients license and internal teams build on. This role sits above our Data Products execution function, which runs day to day feed delivery, pipeline operations, validation, and vendor relationships. Your job is to decide what we build, why it wins, and what it's worth , and to ensure everything we ship is scientifically defensible. You'll own three interconnected product families: Telemetry of Attain’s underlying data asset - technical management of our consumer permissioned transaction and survey data that powers our enterprise business. End-to-end product ownership of the pipeline from Ingestion → Transformation → Consumption in downstream data products. Marketing science feeds - our consumer permissioned transaction data, delivered at the merchant level and CPG product level (UPC/SKU, taxonomy), as the observed ground truth clients measure and activate against. Inclusive of derived data products built on rigorous marketing science: incrementality signals, purchase propensity and ad responsiveness scores (identifying consumers whose brand decisions are genuinely in play), brand switching and loyalty metrics. Extrapolated & predicted data - simulated datasets that project beyond the observed panel: population-level projections, panel-to-population weighting, forecasted behaviors, and predictive scores. You partner with data science that builds the derived and predicted layers. You don't need to fit a doubly robust estimator, but you must be fluent enough in marketing and data science to collaborate on the pod's agenda, pressure test its methods, and make ship/no ship calls based on outputs.

Requirements

Extensive experience across data products, marketing science, ad measurement, or media analytics, inclusive of 3+ years leading teams and at least one role where you owned a commercially licensed data product (feeds, scores, APIs, or datasets) with real revenue and SLAs.
Experience applying AI methods to data extrapolation or similar
Experience managing senior product leaders and technical execution functions you've led leaders, not just ICs.
Working fluency in marketing science and causal measurement: able to evaluate an incrementality methodology, spot selection bias or a broken baseline, and hold your own with PhD level scientists and skeptical client analysts.
Hands on modeling history is a plus; scientific judgment is a must.
Experience directing data scientists: setting priorities, reviewing approaches, and making ship/no ship calls on modeled outputs.
Deep understanding of transaction level data products, merchant and UPC/SKU level structures, taxonomy and categorization, identity resolution and match rates, panel-to-population projection.
Fluency in how advertisers, agencies, and platforms use data: audience activation, measurement, planning, clean room workflows, and identity landscape.
A track record of converting data assets into business outcomes: licensing revenue growth, client adoption.
Executive level communication: equally credible presenting portfolio strategy to the SLT, methodology to a client's data science team, and priorities to engineering.
Experience with consumer permissioned, panel, or purchase graph data and the privacy, consent, and compliance dimensions unique to it.
Familiarity with retail media networks, CTV measurement, unified IDs, and walled garden constraints.
Exposure to the classical marketing science canon (discrete choice, Dirichlet/NBD loyalty math, Markov switching, MMM/attribution) and modern causal ML with healthy skepticism about assumptions.
Data licensing, pricing strategy, and clean room delivery experience (Snowflake, LiveRamp, Databricks, etc.).
Ad tech startup or scale-up experience; comfort operating hands-on where needed.

Nice To Haves

If we don’t see something that immediately fits, we will keep your resume on file for future opportunities.

Responsibilities

Set the data product strategy.
Define the multi year portfolio roadmap across our underlying data asset, as well as our marketing science and extrapolated feeds.
Lead the data product organization.
Manage the Data Product PMs and the execution function responsible for feed delivery, pipeline architecture, QA, match tests, and vendor management, setting standards and unblocking.
Consult with the data science pod to help set priorities.
Translate client and market needs into a modeling agenda; translate model outputs into shippable, explainable data products.
You are accountable for both scientific defensibility and working with your Strategy counterpart to ensure commercial delivery.
Your decision making is rooted in data.
You review data with objectivity to make decisions and go forward plans.
Productize marketing science.
Turn movable middle propensity scoring, incrementality measurement, Markov-based switching and retention metrics, and brand choice modeling into licensed feeds and scores clients can activate and measure with each with a methodology story a sophisticated client's data science team can interrogate.
Apply AI models to our world class data asset.
Use of AI and methods that can materially advance our ability to predict and scale our data.
Treat internal teams as first class customers.
Our measurement, insights, audience, and activation products are built on the underlying data asset.
Champion privacy and permissioned data leadership.
Partner with Compliance and Consumer App product leaders to ensure the portfolio honors consumer permissions and regulatory obligations.