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Form of workGeneral Summary:
Elevate is a technology firm which develops next-generation financial products focused on managing life’s everyday expenses. The Data Science team conceptualizes, develops, deploys, and maintains predictive models using advanced statistical and machine learning methods. These models are used in Elevate’s Underwriting, Account Management, and Operations applications. The Sr. Data Scientist plays a critical role by applying cutting-edge modeling techniques to drive growth, control risk, and ensure operations excellence.
Primary Responsibilities:
- Manage a team of data scientists through predictive models and analytical exercises to deliver business value, innovative approaches and quality execution.
- Design, Develop and Deploy advanced machine learning and Artificial Intelligence algorithms/predictive models for use in Underwriting, Customer Management, Marketing, and Operations;
- Functional lead and point of contact with business partners to support the needs and goals of all Elevate portfolios, Rock teams, and Pods.
- Assess, clean, merge, and analyze large datasets adhering to standardized data manipulation techniques and methodology by leveraging Python, Spark, Snowflakes, etc.
- Design, Develop and Deploy linear, nonlinear, and other ML algorithms for testing, development and deployment into our underwriting engine in the application of risk management in all of Elevate’s acquisition channels;
- Efficiently apply data mining methodologies to minimize credit/fraud losses, maximize response and approval rates, and develop methods to enhance profitability of Elevate products;
- Successfully implement scoring models on multiple decision platforms Including cloud.
- Provide knowledge, insight and guidance of third party data providers such as Transunion, Clarity/Experian and Equifax to include knowledge of products and data available, products to purchase or discontinue, cost benefit analysis of retrospective analysis, effective use of variables, data dictionaries as well as advantages and limitations;
- Maintain clear, detailed model documentation on our Wiki Server by leveraging reproducible research technologies such as Jupyter Notebook, Rmarkdown, etc.
Experience and Education
- Master’s degree in highly quantitative field (Statistics, Economics, Mathematics, Engineering, or other quantitatively-oriented degree). Ph.D. preferred.
- At least six years of experience in Data Science or Modeling for consumer lending, two years of leadership experience in highly quantitative teams; Professional experience waived with at least four years of Data Science or Modeling experience and Ph.D. Degree in highly quantitative field (Statistics, Economics, Mathematics, or other quantitatively oriented degree)
- Superior communication skills for communication with Risk Management peers and executive team
- Proven experience working in fast-paced environment with ever-changing demands
- Demonstrated proficiency with advanced statistical modeling and substantial experience with machine learning techniques (e.g., Random Forest, Gradient Boosting, LASSO, Elastic Net, etc). Knowledge various penalized regression and classification methods a plus;
- Strong data skills, with ability to conduct substantial data munging/engineering.
- Proficiency with Linux, R, Python, or Java; expertise with versioning software (e.g., Git), big data solutions and data processing frameworks (e.g., Spark, Hadoop);
- Experience with database technologies such as Snowflakes, Hadoop, Apache Hive/Impala, Spark, NoSQL, JSON & XML parsing, etc.
- Proficiency of contemporary supervised and unsupervised data mining techniques a plus
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