Senior Analyst, Marketing Science

Double Good•Chicago, IL

1d•Hybrid

About The Position

Double Good is a leading fundraising app and technology company that helps youth organizations raise funds by selling popcorn. This role is for a Senior Analyst, Marketing Science to join the Data team. The position is hybrid, based in the downtown Chicago office, requiring three days per week onsite. The Senior Analyst will serve as the primary marketing data analyst, focusing on understanding and improving organizer acquisition, retention, and marketing return. This role will provide channel, segmentation, and lifecycle analysis to inform marketing decisions. The position expands the Analytics & Insights capabilities by bringing reporting, analysis, and advanced analytics to how Double Good acquires and retains organizers. The analyst will partner with Marketing leaders and maintain a unified view of marketing performance. The work is divided into describing performance (10%), diagnosing drivers of performance (70%), and predicting future outcomes (20%).

Requirements

4+ years in marketing analytics, growth analytics, or marketing science with demonstrated impact on acquisition and retention outcomes.
Expert SQL on a modern cloud data warehouse (Snowflake preferred); working knowledge of dbt.
Proficiency in Python or R for segmentation, cohort, and statistical analysis.
Hands-on marketing measurement: attribution, incrementality, media efficiency, and CAC / LTV / payback, including the limits of each.
Experiment design and measurement, including sample sizing and reading results honestly.
Fluency in Looker or an equivalent BI tool and in product-analytics or eventing data.
Storytelling with data: clear written and verbal communication with marketing and executive stakeholders.
Drives ambiguous projects forward independently.
Bachelor's degree in statistics, economics, marketing analytics, mathematics, or a related quantitative field, or equivalent experience.
Current authorization to work in the United States. Visa sponsorship is not provided.

Nice To Haves

Experience in a direct-to-consumer e-commerce or CPG with repeat customer engagement dynamics.
Churn, propensity, or lookalike modeling in production, and close partnership with a data science team that shipped one.
Familiarity with paid media (Meta, Google, TikTok) and lifecycle marketing (Braze, Iterable, Hubspot) data.
Geo-experiment, marketing-mix modeling, or LTV modeling experience.
Referral, viral, or community-driven marketing analytics.
Use of AI and LLM tools to accelerate analysis and summarize findings.

Responsibilities

Own channel reporting and analysis: performance by youth activity, channel, campaign, and cohort, with certified Looker reporting as the baseline and analysis of what moved and why.
Build organizer, seller, and buyer segmentation: behavioral and value-based segments that Marketing uses to target, message, and forecast.
Lead acquisition, retention, and lifecycle analysis: funnel conversion, repeat and referral behavior, and churn drivers, delivered with Marketing on a biweekly cadence.
Measure marketing spend: channel efficiency, incrementality, and payback (CAC, LTV, ROAS) that inform budget allocation.
Map the customer journey and market development: organizer paths from first touch to repeat fundraiser, and market penetration by geography, youth activity type, and segment.
Build toward predictive: churn scoring and lookalike-organizer models with Data Science, and a learning agenda of experiments (A/B, holdout, geo) with Marketing and Product.
Deliver recommendations, not just reports: translate business questions into data problems and answers into clear, actionable plans for Marketing leadership.