Senior Data Scientist II, Marketing

Zip Co Limited

1d • $162,000 - $175,000 • Hybrid

About The Position

As a Marketing Scientist on our Data Science team, you'll own the models and frameworks that sit at the intersection of customer behavior, marketing and business strategy, and AI. Your work will directly shape how Zip acquires and retains customers, allocates budget, and deploys intelligent agents. The marketing science function at Zip is being built to be genuinely strategic, not a reporting layer. You'll have a direct line to how the business makes decisions about customers and investment, and real ownership over the models and frameworks that inform both.

Requirements

• Advanced degree in a quantitative field (e.g., Mathematics, Physics, Social Science)
• 5+ years of experience in Data Science
• 3+ years of experience applying Data Science to marketing
• Hands-on experience with Marketing Mix Modeling and/or multi-touch attribution in a commercial setting
• Ability to bridge technical depth and business communication, your stakeholders include both engineers and marketing leaders, and you write and speak to both
• Self-starter mentality with strong problem-solving and research skills; able to work independently to drive solutions
• High proficiency in Python and SQL
• Experience with ML/AI frameworks (e.g., XGBoost, LightGBM, Scikit-Learn, Pytorch) and proven track record of leveraging them for business impact
• Proficiency with modern data stacks (e.g., Snowflake, Databricks, dbt)
• Clear alignment with Zip’s core values

Nice To Haves

• Experience designing or contributing to next-best-action or decisioning systems
• Background in consumer fintech or BNPL
• Experience with LLMs and agentic workflows
• Hands-on experience with Spark/PySpark

Responsibilities

• Be the primary owner and driver of Zip’s foundational marketing ML models, including Customer Intent Modeling, Next Best Action Framework, and Marketing Mix Modeling and Attribution.
• Build predictive models that go beyond behavioral pattern recognition to surface the motivations driving customer decisions.
• Determine the appropriate modeling approach, evaluate competing methodologies, and establish standards for model explainability, monitoring, and ongoing refinement.
• Build models designed not just to predict what a customer will do next, but to understand why, so we can meet them at the right moment with something genuinely useful.
• Translate model outputs into concrete, prioritized recommendations that inform customer communications and product experiences.
• Build the decisioning layer that turns predictive insights into actionable recommendations.
• Enable meaningful impact across multiple parts of the organization by helping teams determine the right action for the right customer at the right time.
• Own our Marketing Mix Modeling and multi touch attribution capabilities, giving the Customer organization a reliable, current view of what is actually driving acquisition.
• Translate model outputs into credible investment recommendations that help us allocate budget and resources intelligently across paid, owned, and partner channels.
• Build and maintain the models and methodologies that underpin our understanding of marketing effectiveness and acquisition performance.
• Provide technical guidance and peer review to other data scientists and analysts, share reusable methods, and help raise the quality of marketing science practices across the team.
• Serve as the primary conduit between your work and business stakeholders, translating complex modeling and insights into clear, actionable recommendations.

Benefits

• Flexible working culture
• Incentive programs
• Unlimited PTO
• Generous paid parental leave
• Leading family support policies
• Company-sponsored 401k match
• Learning and wellness subscription stipend
• Beautiful Union Square office with a casual dress code
• Industry-leading, employer-sponsored insurance for you and your dependents, with several 100% Zip-covered choices available

What This Job Offers

• Job Type: Full-time
• Career Level: Senior