Job description

Company Overview: At Snap Finance, we believe everyone deserves access to the things they need, regardless of credit history. Since 2012, we ve used data, machine learning, and a more human approach to create flexible financing solutions that help people move forward. We re proud of our inclusive, supportive culture, built on empowering our customers, partners, and team members alike. When our people thrive, so does our innovation. If you re looking to make an impact and grow with a team that values you, come join us! Job Description Snap Finance is looking to strengthen its dynamic, growing analytics department. We are seeking a dedicated Data Scientist III with a passion for statistics, machine learning, and solving real-world problems with robust data. The ideal candidate understands how to apply the practices of data science to understand a problem and generate significant value through powerful predictive models. This role is tailored for working within a consumer finance company, specifically focusing on supporting the Sales and B2B Marketing departments. This role follows a hybrid work model based out of our Salt Lake City office. Candidates should currently reside in the Salt Lake City area or be willing to relocate within 90 days of their start date. We believe in-person collaboration plays an important role in building strong partnerships, accelerating development, and staying connected as a team. How you’ll make an impact: Experimental Design and Evaluation: 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. Business Impact Analysis: 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. Stakeholder Interaction: 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. Data Mining and Modeling: 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. What you’ll need to succeed: Ability to generate robust statistical analyses (e.g., power analysis, hypothesis testing, experimental design, hierarchical modeling, Bayesian and frequentist methods). 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. 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). 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. Expertise in one or more modeling/machine learning programming languages such as R or Python. What would make you stand out: Experience with a variety of data structures and databases (SQL, no-SQL, graph, etc.). Knowledge about Big Data related techniques (e.g., Map-Reduce, Hadoop, Hive, Apache Spark). Why Join Us: 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 A value-based culture where growth opportunities are endless More: Snap values diversity and all qualified applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. Learn more by visiting our website at www.snapfinance.com . California Residents, please review our California Consumer Privacy Act Notice at snapfinance.com

Requirements

Must-haves

If you re looking to make an impact and grow with a team that values you, come join us!
Experimental Design and Evaluation: 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.
Business Impact Analysis: 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.
Stakeholder Interaction: 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.
Data Mining and Modeling: 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.
Ability to generate robust statistical analyses (e.g., power analysis, hypothesis testing, experimental design, hierarchical modeling, Bayesian and frequentist methods).
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.
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).
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.
Expertise in one or more modeling/machine learning programming languages such as R or Python.
Experience with a variety of data structures and databases (SQL, no-SQL, graph, etc.).
Knowledge about Big Data related techniques (e.g., Map-Reduce, Hadoop, Hive, Apache Spark).

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

machine learningsqlpython

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Paid Time Off

Wellness