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Salary: USD 152,000 - 210,000 / annual
Chime is seeking a Staff Data Scientist to lead marketing analytics and measurement strategy. You will own how Chime measures the effectiveness of its paid marketing efforts and translates those measurements into budget allocation decisions. This is a high-visibility role where your work directly influences marketing, growth, finance, and executive leadership.
Key Responsibilities:
- Own the paid performance story by tracking spend and results across channels weekly, explaining movements in clear, actionable terms for non-technical stakeholders
- Measure new-member acquisition offers to determine whether sign-up incentives bring in higher-quality members and justify their cost through matched-group comparisons
- Set the experimental and offer evaluation framework, defining how tests are designed, interpreted, and evaluated for cost-effectiveness
- Improve marketing attribution by identifying gaps in current rules, proposing better approaches, and partnering with Data Engineering to implement and maintain them
- Translate Marketing Mix Model outputs into budget allocation recommendations across channels
- Estimate the likely impact of new marketing initiatives to help the team prioritize work
- Leverage AI tools to accelerate analysis, validate work, and build reusable assets for the team
- Coach senior data scientists, guide contractors, and establish quality standards for high-stakes analyses
You will work cross-functionally with Marketing, Finance, Data Engineering, and Data Science/ML teams. The role requires both deep technical expertise and strong executive communication skills.
Requirements:
- Bachelor's degree and 10+ years of experience in analytics or data science, with deep expertise in marketing, growth, or consumer products
- Proven track record of setting measurement strategy across multiple teams, not just delivering analyses within a single team
- Deep expertise in marketing measurement: attribution modeling, incrementality testing, marketing mix modeling (MMM), cost per acquisition (CPA), and LTV:CAC ratio analysis, including designing these systems from scratch
- Expert-level experimentation and causal inference skills, including knowing when standard methods are insufficient and building custom approaches
- Strong SQL proficiency and experience shaping data models and pipelines in large data warehouses (Snowflake experience preferred)
- Advanced Python or equivalent language for analysis
- Hands-on experience using Marketing Mix Model output to guide budget decisions at significant scale
- Experience bringing AI tools into analytics workflows for teams, with good judgment about their limitations
- History of influencing VP-level and executive decisions without formal authority, including resolving disagreements between competing stakeholders
- Excellent written and verbal communication for senior, non-technical audiences
- Track record of growing other senior data scientists through mentorship, frameworks, and shared tools
- Comfort with novel, precedent-free problems where you define both the question and the analytical approach
