Sr. Data Scientist

Snap Finance•West Valley City, UT

About The Position

Snap Finance is looking to strengthen its dynamic, growing analytics department. We are seeking a dedicated Sr. Data Scientist 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.

Requirements

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

Nice To Haves

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).

Responsibilities

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.
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.