About the role: We invest heavily every year acquiring small businesses across Meta, Google, TikTok, and email, and we are expanding our marketing analytics function to make every dollar of it accountable. This is a senior, high-visibility role with executive sponsorship at the CEO level, real budget, a strong existing data warehouse, and a clear mandate to take marketing analytics at OPF to the next level and run it.
You will own marketing measurement and marketing data science end to end. That means the sophisticated analytical work that actually moves the spend: multi-touch attribution, return on ad spend by channel and creative, media mix modeling to guide budget allocation, incrementality and geo-lift testing to prove what is truly causal, and predictive models that score the value of a lead before we ever pay to acquire it. You will set the analytical agenda, lead the projects, and bring the recommendations to the table yourself.
You will also own the foundation that this analysis stands on. A measurement stack is only as good as the data feeding it, so you will direct the build of our campaign taxonomy, UTM governance, attribution pipeline, customer data platform, and marketing data model, partnering with data engineering to get it right. You define what good looks like; you do not have to lay every pipe yourself.
What makes this role different from most marketing analytics jobs is what sits downstream of it. We are a lender, so we know far more about our customers than a typical advertiser does. Marketing performance here does not stop at a conversion. It extends through the full funnel to the quality and profitability of the businesses each channel brings us, which gives you a richer signal to optimize against than almost any marketing team gets to work with.
You will lead from day one, supported by a team of data science and analytics talent, and you will help shape that team over time as the function grows. You will run it with a high degree of independence, reporting to and partnering closely with our Senior Director of Business Intelligence, and you will be the analytics counterpart to marketing and growth leadership, expected to bring a point of view rather than a dashboard.
We are a very AI-forward company, and we expect our analytics leaders to work that way. We want someone who leans on modern AI and LLM tooling to move faster and sharper, and who is excited to build intelligence directly into how marketing runs.
We want to work with high-performing badasses who see the whole picture and make everyone around them better.
Responsibilities: - Set and lead the marketing analytics agenda. Own the roadmap from measurement through advanced modeling, prioritize it, and drive it largely independently. This is a role for someone who runs the function and checks in, not someone who waits to be handed the next project.
- Own attribution end to end: take us from last touch to multi-touch models across paid channels, and establish return on ad spend by channel, campaign, and creative as a metric leadership can trust.
- Lead the advanced, causal work: media mix modeling to guide how budget is allocated across channels and to find diminishing-returns curves, plus incrementality and geo-lift experiments that prove what spend is actually causal rather than merely correlated. Know the difference cold, and say which one you are holding when you present a number.
- Own the predictive and data-science work that drives spend: lead-value scoring so the platforms bid toward the businesses worth acquiring, value-based audience segmentation on top of those scores, and predictive ROAS. Direct your data science talent on the modeling and set the standard for it.
- Own how marketing performance is measured across the full funnel, from the ad impression through the website questionnaire to the funded deal and its downstream outcome. You own the definitions and you defend them.
- Direct the build of the measurement foundation with data engineering: campaign taxonomy and naming standards, UTM governance, the attribution pipeline, a customer data platform, and the marketing data model. You own the requirements and the standard; engineering owns the platform.
- Own the analysis of the pre-lead funnel: landing-page and questionnaire step-level tracking, and drop-off analysis by campaign, so we can see where and why prospects fall out before they become a lead.
- Own the KPI and value-realization framework across the marketing project portfolio: for every initiative, what outcome it drives, how it is measured, and what the baseline is. You hold the line on whether a project actually delivered.
- Lead and grow the team. Direct your data science and analytics talent, set the analytical standards, review the work, develop the people, and make the case for additional hires as the function's needs grow.
- Be the analytics voice in marketing planning and budget decisions. Bring recommendations with options, quantified tradeoffs, and a clear read on what we do and do not yet know.
- Present to executives, including the CEO, in plain language with clean visuals and a clear story.
RequirementsQualifications: - 8 or more years in marketing analytics, marketing data science, or growth analytics, including 2 or more years leading analysts or data scientists.
- A track record of running an analytics function or a major analytics program with a high degree of autonomy. You set the agenda, drive it, and keep your leadership informed, rather than waiting for direction. This is the core of the role.
- Bachelor's or Master's degree in a quantitative field (economics, statistics, mathematics, marketing analytics, computer science, or similar); an advanced degree is a plus.
- Deep, hands-on attribution and marketing-measurement experience at real scale on paid digital: Meta and Google, ideally TikTok, at eight-figure annual spend. You know the platform reporting, the conversion APIs, return on ad spend as a discipline, and where all of it misleads you.
- Media mix modeling. You have built or led MMM to guide budget allocation across channels, including diminishing-returns and saturation curves, and you can explain honestly what it can and cannot answer.
- Incrementality and geo-lift testing. You have designed and read true incrementality experiments (geo-based lift studies, holdouts, matched markets) and you treat them as the causal ground truth that calibrates correlational attribution.
- Genuine data-science depth, not just reporting. You are fluent in the modeling this function runs on: multi-touch attribution, predictive lead-value and lifetime-value scoring, value-based segmentation, and predictive ROAS. Enough to direct a Lead Data Scientist on this work and review it critically: stress-test the assumptions, catch a flawed model, and defend the methodology to executives. Hands-on coding of the models yourself is not the expectation.
- Strong statistical and experimental judgment. You validate a signal before you act on it, you separate a channel being bad from a channel serving a harder segment, you distinguish correlation from causation, and you do not crown a winner off a single cut of the data.
- Strong SQL and working Python. You can pull the data and do the analysis yourself in pandas or notebooks; you are not waiting on someone to build you a dashboard first.
- You have stood a measurement stack up, not only operated one: campaign taxonomy and event dictionaries, UTM governance, server-side conversion tracking (Meta CAPI, Google Enhanced Conversions), GA4 or equivalent, and a warehouse-based marketing data model. You can own the requirements and the standard while data engineering builds the platform.
- Experience with a customer data platform, either packaged (Segment or similar) or warehouse native and composable (Hightouch, Census, or similar), and a view on when each is the right call.
- Excellent business judgment and executive communication. You can take a complex analytical problem and hand leadership a decision, and present it to executives, including the CEO, in plain language.
- Experience in lending, fintech, insurance, or another business with a long funnel and an underwriting or qualification gate between the lead and the revenue (a strong plus). Experience connecting marketing performance to downstream unit economics, such as profitability, loss, or lifetime value, rather than stopping at conversions (a strong plus).
- A real understanding of modern AI (LLMs, agents, MCPs) and comfort building lightweight tooling on your own data (a plus).
- Experience with dbt and cloud data environments, AWS in particular (a plus).
Benefits- Local & National Health Insurance
- Dental and Vision insurance
- Group Medical Bridge
- 401k with Match
- ID Protection: 100% covered by the company
- Life Insurance: 100% covered by the company
- Generous PTO and holidays
