Data Scientist

Snap Finance

Data Scientist III at Snap Finance applying machine learning to analyze datasets and provide insights. Building predictive models and collaborating with Sales and B2B Marketing teams.

Posted 6/18/2026full-timeWest Valley City • Utah • 🇺🇸 United StatesMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

Mining, modeling, and analyzing large datasets, utilizing predictive modeling techniques.
Building and validating a variety of statistical models, providing analytic support, and developing new criteria and/or strategies.
Design and implement experiments and processes for evaluating business performance, new products, and product features.
Conducting required analyses incorporating project design, data collection, and analysis, summarizing findings, and presenting results in an understandable manner.
Compiling appropriate data, applying multidimensional data aggregation, and performing profile analysis to evaluate business impact.
Handling large volumes of transaction-level data to derive actionable results efficiently.
Interacting with stakeholders to understand their business questions, crafting methodologies to mine/analyze datasets, and delivering insightful recommendations.
Keeping up to date with the latest technology trends.

Requirements

What you’ll need

3-5 years working in a data science position or performing work that aligns with the required skills in another position.
M.S. in quantitative fields such as Statistics, Econometrics, Mathematics, Physics, Computer Science, Quantitative Social Science, Quantitative Finance, or another related field.
B.S. in the fields described above will be considered if the skill set and experience are robust.
Expertise in one or more modeling/machine learning programming languages such as R or Python.
Strong SQL skills and the ability to extract data from non-relational data sources.
Advanced understanding and professional experience with the following methods: Classification methods (e.g., Neural Net, Logistic Regression, Decision Trees, KNN, Random Forest).
Regression methods (e.g., Linear, Nonlinear, Boosted Regression Trees).
Clustering methods (e.g., K-means, Fuzzy C-means, Hierarchical Clustering, Mixture Modeling).
Ability to generate robust statistical analyses (e.g., power analysis, hypothesis testing, experimental design, hierarchical modeling, Bayesian and frequentist methods).
Demonstrated ability to take data science projects from development to production.
Skilled analyst who produces regular reporting content for key stakeholder meetings, responds to ad hoc analysis requests, and generates insightful deep dives.
Familiarity and experience with concepts in consumer finance, sales operations, and B2B marketing methods.

Benefits

Comp & perks

Generous paid time off
Competitive medical, dental & vision coverage
401K with company match for US
Company-paid life insurance
Company-paid short-term and long-term disability
Access to mental health and wellness resources
Company-paid volunteer time to do good in your community
Legal coverage and other supplemental options

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

predictive modelingstatistical modelsdata analysismultidimensional data aggregationRPythonSQLclassification methodsregression methodsclustering methods

Soft Skills

analytical skillscommunication skillsstakeholder interactionmethodology craftingreportingproblem-solvingpresentation skillsproject managementcollaborationadaptability