Senior Marketing Science Analyst

JD Finish Line

11d•$70,000 - $90,000

About The Position

We are seeking a highly analytical and strategic individual to serve as the primary quantitative leader for our Digital Marketing organization. As the Senior Marketing Science Analyst, you will build and own the growth analytics function, setting the foundation for how we measure, model, and make decisions across the entire digital business - building the predictive models (Retention/Churn, CLV) and incrementality frameworks (MMM/Geo-Lift/DDA) that dictate how we spend our next million dollars. As a direct report to the VP, you are not just a reporter of performance—you are a strategist who uses advanced mathematics to engineer growth and operationalize insights across every digital touchpoint. This is a high-impact, performance-based role designed to move beyond standard reporting. While we have established baseline infrastructure, you are expected to take our capabilities to the next level—building the predictive and causal models that directly engineer profitable growth. You will serve as the "analytical architect," responsible for unlocking deep customer insights, increasing the validity and speed of our KPIs, and translating complex data into clear, actionable strategies for the VP and broader marketing team. This individual will effectively exhibit JD Finish Line’s core values of Customer, People, Winning, Community, and Financial Responsibility in everything they do by performing the following main duties: Growth & Performance Architecture Strategic Partnership: Act as the primary analytical partner for paid and retention channel owners to optimize performance and unlock channel-level efficiencies. Full-Funnel Ownership: Build and own the marketing analytics function, establishing how we measure the core drivers of the business and the impact of our actions on those drivers. Advanced Attribution: Transition the organization from baseline models to sophisticated Multi-Touch Attribution (MTA) and Market Mix Modeling (MMM) to drive profitable acquisition. Incrementality Testing: Design and lead "Scientific Lift" studies (e.g. Geo-tests) to measure the true causal impact of Meta, Google, and Brand campaigns. Market Mix Modeling (MMM): Maintain the statistical model that advises the VP on quarterly budget shifts between Paid, Owned, and Earned media. Full-Funnel Attribution: Manage the transition from basic last-click reporting to a multi-touch, data-driven attribution (DDA) model. Customer Science & Program Development Model Development: Lead the development of programs that target customers across the full lifecycle using advanced statistical modeling, machine learning, and propensity scoring. Predictive Insights: Build and maintain predictive models (Churn, CLV, Next-Best-Action) that allow the CRM/retention team to automate 1-to-1 personalization. Data Harmonization: Collect, harmonize, and review data across disparate sources (Commerce Tools, Looker/Big Query, Meta, GA4, Braze) to ensure a unified view of the customer. Strategic Partnership & Team Enablement Paid Media/Acquisition: Provide the Paid Media team with data-driven insights on bid optimization, budget allocation, and target audiences derived from MMM, MTA and CLV forecasts to lower blended Customer Acquisition Cost (CAC) while scaling volume. CRM/Retention: Collaborate closely with the CRM/Lifecycle team to operationalize models by feeding predictive scores directly into segmentation and personalization engines (e.g., Braze) to maximize conversion and retention rates. Executive Insight: Serve as the strategic analytical consultant to leadership, translating complex mathematical findings into clear, concise, and actionable business recommendations for investment decisions. Data Strategy: Partner with Data Engineering and IT teams to architect and ensure the integrity, governance, and flow of data from raw sources (E-commerce platform, Ad Platforms, appsflyer, etc) into the cloud data warehouse for modeling purposes. Experimentation & Rigorous Analysis Scientific Testing: Design and execute rigorous experiments (A/B, Multivariate, and Geo-Lift) to isolate the true causal impact of marketing initiatives. Visual Storytelling: Formulate key insights, charts, and reports for high-level presentations to the VP and executive leadership, turning complex coefficients into business narratives. KPI Visibility: Increase the transparency and accuracy of core business metrics, ensuring the team is making decisions based on "valid" and "real-time" data.

Requirements

B.S. or M.S. in a quantitative field (Statistics, Economics, Mathematics, or Data Science).
4-8 years in Marketing Analytics or Data Science, with at least 3 years in a high-volume Retail/E-commerce environment.
Proven ability to translate complex data into business strategy and present to executive-level stakeholders.
Experience working independently on analytical tasks, building, and optimizing ‘big data’ pipelines, architectures, and data sets
Experience developing and delivering exec-level presentations using insights derived from analytics
Strong working knowledge of Microsoft PowerPoint and Excel
Expert proficiency in SQL and Python
Expert experience with Google Analytics 4 (GA4) and a major BI/Visualization platform (preferably Looker).
Direct working experience with the data output of a major CRM/E-commerce platform (e.g., Commercetools, Braze, etc).
Mastery of Looker to build executive-level dashboards.
Sit for more than 6 hours per shift
Use hands to finger, handle and feel
Reach with hands and arms
Talk and/or hear
Stand for up to 2 hours at a time regularly
Walk or move from one location to another regularly
Periodically may need to climb, balance, stoop, kneel, or crouch
Lift and/or move up to 10 pounds regularly and up to 50 pounds occasionally
Punctuality and regular attendance consistent with the company’s policies are required for the position.
Average workweek is 40 hours, which can vary depending on business need.

Responsibilities

Act as the primary analytical partner for paid and retention channel owners to optimize performance and unlock channel-level efficiencies.
Build and own the marketing analytics function, establishing how we measure the core drivers of the business and the impact of our actions on those drivers.
Transition the organization from baseline models to sophisticated Multi-Touch Attribution (MTA) and Market Mix Modeling (MMM) to drive profitable acquisition.
Design and lead "Scientific Lift" studies (e.g. Geo-tests) to measure the true causal impact of Meta, Google, and Brand campaigns.
Maintain the statistical model that advises the VP on quarterly budget shifts between Paid, Owned, and Earned media.
Manage the transition from basic last-click reporting to a multi-touch, data-driven attribution (DDA) model.
Lead the development of programs that target customers across the full lifecycle using advanced statistical modeling, machine learning, and propensity scoring.
Build and maintain predictive models (Churn, CLV, Next-Best-Action) that allow the CRM/retention team to automate 1-to-1 personalization.
Collect, harmonize, and review data across disparate sources (Commerce Tools, Looker/Big Query, Meta, GA4, Braze) to ensure a unified view of the customer.
Provide the Paid Media team with data-driven insights on bid optimization, budget allocation, and target audiences derived from MMM, MTA and CLV forecasts to lower blended Customer Acquisition Cost (CAC) while scaling volume.
Collaborate closely with the CRM/Lifecycle team to operationalize models by feeding predictive scores directly into segmentation and personalization engines (e.g., Braze) to maximize conversion and retention rates.
Serve as the strategic analytical consultant to leadership, translating complex mathematical findings into clear, concise, and actionable business recommendations for investment decisions.
Partner with Data Engineering and IT teams to architect and ensure the integrity, governance, and flow of data from raw sources (E-commerce platform, Ad Platforms, appsflyer, etc) into the cloud data warehouse for modeling purposes.
Design and execute rigorous experiments (A/B, Multivariate, and Geo-Lift) to isolate the true causal impact of marketing initiatives.
Formulate key insights, charts, and reports for high-level presentations to the VP and executive leadership, turning complex coefficients into business narratives.
Increase the transparency and accuracy of core business metrics, ensuring the team is making decisions based on "valid" and "real-time" data.