Data Scientist II

Snap Finance•Utah, UT

2d•Remote

About The Position

Snap Finance is looking to strengthen its dynamic, growing analytics department. We are seeking a dedicated Data Scientist with a passion for statistics, experimentation, and solving real-world problems. The ideal candidate is an analytically driven problem solver who can translate data into actionable business insights. In this role, you will partner with Marketing to support customer acquisition, engagement, retention, and lifetime value initiatives across multiple financial products. You'll leverage data science, experimentation, customer segmentation, personalization, and marketing measurement techniques to help drive growth and improve customer experiences.

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:

Nice To Haves

Experience supporting customer growth, CRM, or lifecycle marketing initiatives, including audience segmentation, personalization, and campaign measurement across channels such as email, SMS, push notifications, paid media, or direct mail.
Hands-on experience designing and analyzing A/B tests and marketing experiments, with the ability to translate results into actionable recommendations that improve acquisition, engagement, or retention.
Familiarity with AI and machine learning applications in marketing, such as customer propensity modeling, personalization, forecasting, or using AI-powered tools to accelerate analysis and insight generation.

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 impact, and help shape the future across the organization.