Data Scientist, Marketing Analytics
On-siteAustin, Texas, United States
Job Summary
Data Scientist, Marketing Analytics role at Sonar focused on turning marketing data into actionable decisions. Own marketing attribution, ROI, and conversion analysis end-to-end; diagnose drops and anomalies, quantify drivers, and translate findings into clear recommendations for marketing leadership. Design and interpret experiments (A/B tests), partner with Data Engineers and Analytics Engineers to define data models and pipelines that connect marketing data with sales and product usage, and communicate insights to non-technical stakeholders while documenting context, caveats, and decisions. Collaborate within the Data & Insights team and with Marketing, Marketing Ops, IT/Data, and other data science domains to drive spending optimization and impact across channels and campaigns.
Required Qualifications
- Strong analytical track record: someone who has measurably influenced business or marketing decisions through analysis, not just produced reports.
- Comfort with the latest AI tools, and a habit of using them to work faster and sharper: exploring data, writing and debugging code, drafting analysis, and accelerating insight.
- Eagerness to develop: you actively grow your skills, seek feedback, and treat new tools and methods as opportunities rather than threats.
- Solid SQL. You can independently query, join, and explore data without waiting for someone to prepare it for you.
- Proficiency in Python for analysis, modeling, and automation.
- Statistical foundation: experimentation, significance testing, regression, segmentation, forecasting, and the judgment to know which applies.
- Working knowledge of marketing and GTM data: channels, campaigns, attribution models, funnel and conversion metrics, and the realities of joining marketing data to CRM/sales and product usage data.
- Willingness to get hands-on with data modeling. You don't need to be a dbt expert, but you must be comfortable exploring messy data and partnering on (or building) the models you need rather than waiting for clean tables.
- Strong communication and stakeholder skills: you can challenge weak measurement respectfully and make a recommendation, not just present options.
- Proactivity and autonomy: you raise your hand early, plan your own work, and look for impact without being asked.
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