Partner with other data scientists, analytics engineers, and business stakeholders to generate actionable insights that inform paid media strategy and execution
Contribute to the development and enhancement of marketing attribution models (e.g., MTA, MMM, LTV), working closely with stakeholders to define MMM inputs, establish modeling constraints, validate findings, generate marginal ROAS curves, and translate insights into actionable budget scenario planning.
Design and implement experiments and statistical models to evaluate incrementality, lift, and media effectiveness, including delivering high-profile experimentation readouts to senior stakeholders.
Build and maintain data pipelines and dashboards that enable data-informed financial decisions and optimize ROAS and customer LTV
Translate ambiguous business problems into structured analytical approaches and deliver high-impact solutions independently
Write production-quality code (Python, SQL, etc.) to manipulate and analyze large-scale datasets
Communicate clear, data-driven recommendations to both technical and non-technical partners

Requirements

4+ years of experience in a data science role, with a degree in economics, statistics, or a related quantitative field.
Deep understanding of paid media and digital advertising ecosystems, with hands-on experience in marketing analytics.
Proven experience building and evolving Marketing Mix Models (MMM), translating model outputs into marginal ROAS curves, budget allocation recommendations, and scenario planning guidance.
Experience developing and applying multi-touch attribution (MTA) methodologies to inform channel and campaign performance optimization.
Strong track record designing and analyzing A/B tests and incrementality experiments to measure lift and causal impact.
Strong foundation in statistics and machine learning, with the technical depth to perform advanced analytics and build robust models.
Experience building and maintaining data pipelines (e.g., DBT) and developing scalable dashboards in tools such as Tableau and/or Looker to enable self-serve insights.
Proven ability to solve ambiguous, loosely defined problems and translate them into structured, data-driven solutions.
Ability to operate independently with minimal oversight while delivering high-quality, reliable work.
Skilled at building relationships, leading strategic data-driven discussions, and identifying opportunities to support business growth.
Clear communicator who can translate complex technical concepts into simple, actionable insights to non-technical audiences.
Motivated to work alongside AI tools, with foundational LLM knowledge and awareness of emerging concepts (e.g., MCP, agent-based systems) and their productivity implications.

Benefits

health insurance
six month paid parental leave
401(k) retirement plan
monthly meal allowance
23 paid days off
13 paid flexible holidays

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonSQLMarketing Mix Models (MMM)Multi-Touch Attribution (MTA)A/B testingIncrementality experimentsStatisticsMachine learningData pipelinesData analysis

Soft Skills

Problem-solvingIndependent workCommunicationRelationship buildingStrategic thinkingData-driven decision makingAnalytical thinkingCollaborationLeadershipAdaptability