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Metro location, generalist Data Scientist title, mid-level experience range, and known agency brand increase applicant competition.
Strong marketing/media measurement and MMM requirements make cross-industry transfers harder, increasing domain specificity.
Explicit 2–5 years plus mandatory Python, SQL, GCP, MMM and marketing domain experience raises screening rigidity.
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Build and maintain predictive models like lead scoring and churn prediction to support sales, marketing, and retention efforts.
Develop and execute Media Mix Modeling studies to measure and optimize marketing channel effectiveness and budget allocation.
Design, implement, and productionize data pipelines and machine learning models using Python and Google Cloud Platform services, translating outputs into actionable marketing recommendations.
2–5 years of relevant experience in data science or analytics roles.
Strong proficiency in Python for data analysis and machine learning.
Solid SQL skills with experience handling large, complex marketing and transactional datasets.
Experience with Google Cloud Platform (especially BigQuery) and knowledge of media mix modeling, lead scoring, and churn prediction models.
Experienced in marketing and media analytics, with understanding of marketing concepts such as campaigns, funnels, attribution, and KPIs.
Capable of bridging technical data science outputs and non-technical stakeholder communication, especially within client servicing and media planning teams.
Prior exposure to media agency, ad-tech, or martech environments and familiarity with marketing measurement frameworks and platforms (e.g., Google Ads, GA4, ADH) preferred.