Data Scientist II

About the role

Snap Finance is seeking a dedicated Data Scientist to join its growing analytics department. The ideal candidate will bring a passion for statistics, experimentation, and solving real-world problems to support customer acquisition, engagement, retention, and lifetime value initiatives.

Responsibilities

• Analyze customer, campaign, and behavioral data to uncover opportunities for customer acquisition, engagement, retention, and growth.
• Support the development of audience segments and personalization models that enhance customer experiences across digital, paid media, SEO, email, SMS, push notifications, in-app messaging, and direct mail.
• Partner with marketing teams to better understand customer behavior and identify opportunities to improve campaign performance.
• Build and enhance attribution models that measure the incremental impact of marketing activities and customer touchpoints across channels and financial products.
• Analyze cross-channel marketing performance to identify opportunities for improved targeting, personalization, and return on marketing investment.
• Communicate attribution findings and performance insights in a clear, actionable manner that informs marketing strategy and business decisions.
• Assist in designing, executing, and analyzing experiments to measure the effectiveness of marketing campaigns, customer journeys, and personalization efforts.
• Evaluate test results and provide recommendations that improve customer acquisition, engagement, retention, and marketing efficiency.
• Contribute to a culture of continuous learning and optimization through data-driven decision-making.
• Apply statistical, predictive, and analytical techniques to solve marketing and customer-focused business problems.
• Build, validate, and refine models that support audience targeting, personalization, customer growth, and retention initiatives.
• Work with large customer and marketing datasets to generate insights and support business decisions.
• Collaborate closely with stakeholders across Marketing, Engineering, Product, and Analytics to understand business needs and deliver meaningful insights.
• Communicate analytical findings and recommendations in a way that is accessible to both technical and non-technical audiences.
• Build strong working relationships and develop a deep understanding of the business, customers, and marketing goals.
• Leverage AI-powered tools to improve productivity, accelerate analysis, support insight generation, and enhance marketing effectiveness.
• Explore new analytical approaches and emerging technologies while applying sound business judgment and analytical rigor.
• Take on increasingly complex challenges, broaden your impact, and help shape the future across our organization.

Requirements

• 2–3 years of experience working in a data science position or performing work that aligns with the required skills in another role.
• M.S. in a quantitative field such as Statistics, Econometrics, Mathematics, Physics, Computer Science, Quantitative Social Science, Quantitative Finance, or another related field.
• B.S. in one of the fields described above will be considered if the candidate's skill set and experience are robust.
• A skilled analyst who produces regular reporting content for key stakeholder meetings, responds to ad hoc analysis requests, and generates insightful deep dives.
• Familiarity with and experience in consumer finance, digital marketing, SEO, and/or direct-to-consumer marketing.
• Classification methods such as logistic regression, decision trees, KNN, and random forests.
• Regression methods such as linear regression, nonlinear regression, and boosted regression trees.
• Clustering methods such as k-means, hierarchical clustering, and mixture modeling.
• Ability to generate robust statistical analyses, including power analysis, hypothesis testing, experimental design, hierarchical modeling, and Bayesian and frequentist methods.
• Demonstrated ability to take data science projects from development to production.
• Expert SQL skills and the ability to extract data from non-relational data sources.
• Expertise in one or more programming languages such as Python or R.
• Advanced understanding and professional experience with the following methods: