Marketing Science Analyst

Partner with Marketing and Media teams to translate business challenges into structured analytical problems, identifying opportunities to expand measurement capabilities (e.g., controlled experimentation design, upper vs. lower funnel effect, portfolio-level product marketing analysis, geographic modeling).

Investigate and evaluate new data sources for marketing mix modeling and other analytics use cases, such as media execution and attribution metrics, third-party competitive and market research, internal CRM, web analytics, and transaction data.

Assess data quality, completeness, and granularity of new data sources; determine which metrics should be incorporated into models and why (e.g., impressions for reach-based analysis).

Collaborate with Data Scientists to define model specifications, variable selection, and transformation logic, and recommend new modeling capabilities such as geographic models, impression-based models, and channel interaction effects.

Design and analyze marketing experiments (geo tests, holdout tests) to validate model recommendations and measure incrementality.

Validate model inputs and outputs from a business perspective; conduct deep-dive analyses to explain model findings and identify drivers of KPI changes.

Create visualizations and dashboards using Tableau, or Python/R libraries.

Serve as liaison between Marketing/Media teams and Data Science team; translate technical model outputs into actionable marketing recommendations and present insights to cross-functional audiences.

Train marketing teams on interpreting MMM results and using optimization tools; create documentation and manage relationships with external media agencies and data vendors.

Bachelor's degree in Marketing, Business, Economics, Statistics, Mathematics, or related field.

Master's degree (MBA, MS in Marketing Analytics) preferred.

3-5 years of experience in marketing analytics, media planning, digital marketing, or marketing strategy roles, preferably in financial services.

Strong understanding of media planning and buying across traditional (TV, radio, print, OOH) and digital channels (display, video, social, search, programmatic); knowledge of media metrics (impressions, reach, GRPs, CPM, CPA, ROAS).

Experience with marketing mix modeling tools (Meta Robyn, Google Meridian) or exposure to MMM projects is a plus.

Understanding of causal inference concepts (incrementality, test-and-learn) is a plus.

Experience with geo-experiments, matched market tests, or A/B testing programs is a plus.

Understanding of customer lifetime value (CLV) modeling and attribution models (multi-touch, data-driven) is a plus.

Bachelor's Degree