Data Scientist II - Advertising, Growth Marketing

Spotify•New York, NY

4d•Remote

About The Position

Our mission on the Advertising Product & Technology team is to build a next generation advertising platform that aligns with our unique value proposition for audio and video. We work to scale the user experience for hundreds of millions of fans and hundreds of thousands of advertisers. This scale brings unique challenges as well as tremendous opportunities for our artists and creators. We’re looking for a Data Scientist to join Spotify Advertising’s Growth pod—a team of data scientists and analytics engineers focused on unlocking insights and building scalable data products that power advertisers and revenue growth. Our work spans lead scoring, attribution modeling, metric design, experimentation, and causal inference. As we increase investment in paid media and continue scaling automated buying channels, we’re entering our next phase of growth. While we’ve built strong foundations—including multi-touch attribution (MTA), lifetime value model (LTV) and marketing mix modeling (MMM) — there's a significant opportunity to deepen our insights, refine our models, and drive greater impact. In this role, you’ll help expand and operationalize our marketing science toolkit—expanding model capabilities and ensuring they inform clear, outcome-driven decisions. Your work may span: Marketing Attribution Models, Paid Media Spend Optimization, Growth Analytics, Forecasting & Scenario Modeling, Incrementality & Lift Measurement, Insights Automation & Dashboards, Data Infrastructure & Signal Engineering. You’ll play a key role in turning complex data into scalable solutions that directly influence how we invest, optimize, and grow.

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.

Responsibilities

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