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This position will lead Elevate’s Data Science efforts to develop and maintain data assets, decision processes, and custom credit scoring algorithms for use across the business. The Sr. Director works cross functionally with IT, Risk, and portfolio teams, as well as with other data scientists to develop, evaluate, and implement data and scoring products to drive underwriting, strategy, and decisions. The Sr. Director develops and implements data structures required for advanced algorithms and for decisioning logic.
Principal Duties and Responsibilities:
Manage and lead a team of data scientists who develop data and algorithms.
Conduct research and development efforts to improve model and algorithm performance using new methods and data.
Develop data and algorithms to support improvements in through-the-door approvals, fundings, fraud reduction, loss rates, and other key metrics for each of Elevate’s products.
Identify, create, and implement novel data sources and research new scores for inclusion in Elevate’s progression of internally-developed predictive models.
Collaborate with Risk Management and Product leaders to develop data requirements for all products and channels.
Partner with IT resources to ensure effective curation and governance of data resources.
Develop and maintain strong relationships with IT, Risk Management, and other support functions to ensure alignment of Data Science initiatives with Company objectives.
Complete all other projects as assigned.
Experience and Education:
Advanced degree (MA/MS/MBA/PhD/DSc) in an analytical field required.
6+ years of experience developing and deploying advanced machine learning and scoring algorithms.
Demonstrated experience in data development and management in advanced databases.
Management experience, preferably of large groups of quantitative analysts and modelers.
Required Skills and Abilities:
Ability to thrive in a dynamic and fast-paced environment and drive change, and collaborate effectively with a variety of individuals and teams.
Knowledge of credit and credit bureau data.
Demonstrable expertise with machine learning techniques and algorithms: decision trees, penalized regressions, bootstrap aggregation, boosting, model ensembling, model stacking, and other methods of contemporary predictive analytics.
Demonstrable expertise with multivariate statistical methods, including cluster analysis, linear PCA, and factor analysis.
R, Python, and other high-level languages; C++ a plus.
Familiarity with RESTful APIs and low-latency scoring technologies.
Strong understanding of databases, database architecture, and high performance computing frameworks (Spark, Hadoop)
Strong analytical, statistical, and problem solving skills.
Strong planning, organizational, people, and project management skills.
Excellent interpersonal, verbal and written communication skills.