Swoon is hiring for our client based out of Washington D.C. and this will be a hybrid role.

This is a 12-month contract opportunity to start with Benefits - Health, Dental & Vision (50% of premium covered for contractor).

Job Overview:

This role requires a candidate with strong programming, data management, and statistical expertise. You will play a key role in supporting in-market control tests and developing machine learning models that drive subscription growth, increase revenue, and enhance app engagement and customer experience. Responsibilities include building predictive models for customer churn and lifetime value (LTV), improving segmentation and personalized messaging, and measuring the effectiveness of advertising spend. An interest in leveraging data science to optimize digital media and conversions is essential.

Key Responsibilities:

Marketing Testing: Design and support marketing tests to maximize media spend efficiency, improve app engagement, and increase subscriber retention across the customer lifecycle. This includes experimental design, sample sizing, timing, quality checking, and test evaluation.
Data-Driven Insights: Create and present experimental results and recommendations to business stakeholders, using dashboards to provide daily monitoring and insights.
Predictive Modeling: Build machine learning models to forecast key outcomes and support marketing strategies, focusing on digital acquisition and optimizing the user journey.
Collaboration with Engineering: Partner with engineering teams to productionize and monitor model performance, ensuring seamless deployment and stability.
Cross-Functional Analytics Representation: Act as a Commercial Analytics expert within cross-functional teams to support data-driven decision-making and achieve business objectives.
SQL Development: Develop complex SQL queries to integrate data from diverse sources, preparing data for feature engineering and test analysis.
Automation and Tool Development: Create custom Python libraries for team use, enabling efficient application of statistical methods for experimental design, bias checking, and matched-market testing.

If interested in more details, please apply!

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