Data Scientist, Marketing Analytics

What you will do

Drive marketing decisions with analysis. Own attribution, ROI, conversion, and funnel analysis. Diagnose drops and anomalies, quantify what drives them, and translate findings into clear recommendations for marketing leaders.

Optimize marketing spend. Connect spend to outcomes across channels and campaigns, and advise on where to invest, cut, or test next.

Be proactive. Explore and correlate data to surface insights nobody asked for. Anticipate the next question. Bring opportunities to the marketing team rather than waiting for requests.

Run experiments. Design and read out A/B tests and other experiments, applying sound statistical methods and being honest about significance and causality.

Partner on the data foundation. Work hand in hand with Data Engineers and Analytics Engineers to get marketing data into the warehouse and modeled. Define requirements, explore raw data, and contribute to data models that connect marketing data with sales and product data.

Tell the story. Communicate insights so non-technical stakeholders can act, and document context, caveats, and decisions so the work survives handoffs.

Experience and 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. You stay current as the tooling evolves and bring new approaches to the team.

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.