Klover
Vice President of Data Product
- Compensation
- Not disclosed
- Location
- Redwood City, United States; New York, United States; Chicago, United States
- Work setup
- Not specified
- Seniority
- C level
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Not specified
- Posted
Skills
Full description
- 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
- 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
- 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
- How We’ll Measure Success:
- Data product revenue growth, adoption, and retention with expansion driven by trust in the data’s quality and methodology
- Documented, validated methodologies behind every derived and predicted field, able to withstand advertiser, agency, and academic scrutiny
- A scaled, high-functioning data product org: reliable delivery through the execution team, a productive DS pod with an agenda tied to the roadmap, and clear career paths
- Internal consolidation: fewer one-off extracts, more products built on canonical feeds, high internal customer satisfaction
- External recognition of Attain’s data as the credible standard for consumer permissioned purchase data
- Experience directing data scientists: setting priorities, reviewing approaches, and making ship/no ship calls on modeled outputs
- Experience applying AI methods to data extrapolation or similar
- Fluency in how advertisers, agencies, and platforms use data: audience activation, measurement, planning, clean room workflows, and identity landscape
- 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
- Experience with consumer permissioned, panel, or purchase graph data and the privacy, consent, and compliance dimensions unique to it
- A track record of converting data assets into business outcomes: licensing revenue growth, client adoption
- Familiarity with retail media networks, CTV measurement, unified IDs, and walled garden constraints
- 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
- Experience managing senior product leaders and technical execution functions you’ve led leaders, not just ICs
- Data licensing, pricing strategy, and clean room delivery experience (Snowflake, LiveRamp, Databricks, etc.)
- 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
- Executive level communication: equally credible presenting portfolio strategy to the SLT, methodology to a client’s data science team, and priorities to engineering
- Ad tech startup or scale-up experience; comfort operating hands-on where needed
- 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
- We encourage you to apply, even if your experience doesn’t match every detail on the job description