Marketing Analytics & Data Science Manager

Leading Response helps clients in financial services, healthcare, legal, and senior living connect with high‑intent consumers through data‑driven, omnichannel marketing. Our consumer‑facing campaigns reach millions each month through direct mail, live events, digital ads, and more.

The Marketing Analytics & Data Science Manager owns the insight, modeling, and activation layer that drives performance and efficiency across our media programs. This role blends traditional marketing analytics (reporting, dashboards, attribution) with applied data science (predictive modeling, segmentation, propensity scoring) and hands‑on activation strategy across paid digital channels. The ideal candidate is equally comfortable building a Tableau or Power BI dashboard, training a lead‑scoring model, and advising a media buyer on how to shift a Google Ads or Facebook budget based on what the data says.

This person will also partner closely with our Data Engineering team to ensure the underlying data infrastructure (AWS data lake, pipelines, feature stores) supports both reporting and model deployment needs.

This position will be based in Tampa, FL or Atlanta, GA.

• Develop reporting requirements and documentation to support ongoing business intelligence needs.
• Pull, clean, blend, and automate data from multiple sources (media platforms, CRM systems, call tracking, GA4, etc.) into Tableau and/or Power BI.
• Build and maintain dashboards that clearly communicate performance trends, KPIs, and business impacts.
• Ensure data quality, consistency, and reliability across reports and datasets.
• Configure and maintain GA4 properties, conversions, audiences, event tagging, and reporting views.
• Evaluate attribution methodologies and understand how performance indicators vary across channels.
• Design, build, and validate predictive models supporting lead scoring, propensity to convert, churn/retention risk, and customer lifetime value.
• Apply segmentation and clustering techniques to identify high‑value audience segments for targeting and suppression.
• Develop and maintain model monitoring processes to track performance drift and retraining needs.
• Translate model outputs into practical scoring, targeting, or bidding logic that media teams can act on.
• Partner with Data Engineering to product ionize models, ensuring reliable data pipelines, feature availability, and refresh cadence.
• Advise media specialists on how to apply analytics and model outputs directly within Google Ads and social channels like Facebook/Meta (audience targeting, bid strategy, lookalike/similar audience creation, budget allocation).
• Measure and report on incremental lift from Google Ads and social channel campaigns (e.g., Facebook/Meta), using lift studies, holdout tests, and geo experiments to isolate true incremental impact from correlation.
• Partner with digital marketing teams to design and execute test‑and‑learn strategies, including experiment setup, measurement plans, and performance readouts.
• Recommend channel mix and budget shifts based on model‑driven forecasts of efficiency, ROI, and measured lift.
• Support the integration of first‑party and modeled audience data into ad platform activation (Google Customer Match, Meta Custom Audiences, etc.).
• Work with marketing and product teams to ensure accurate tracking alignment from click to lead to conversion.
• Collaborate closely with Data Engineering on data architecture, pipeline reliability, and access to clean, model‑ready datasets.
• Collaborate across departments (Consumer Marketing, Sales, Client Services, Creative, IT) to ensure organizational alignment and effectiveness of marketing activities.
• Communicate insights and model recommendations to media specialists responsible for day‑to‑day campaign management.
• Translate complex data and model output into simple, compelling stories for non‑technical audiences.
• May provide leadership or mentoring to analysts (role can grow into people management).
• Present insights to internal teams and occasionally external clients.

• Bachelor’s degree in Marketing, Data Science, Statistics, Analytics, Computer Science, or related field; advanced degree a plus.
• 6‑10+…