Staff Data Scientist, Octane

• 7+ years in a data science role (or PhD in a quantitative field with 3+ years as a senior data science or comparable role)
• High Fluency with SQL, SparkSQL Python, PySpark and statistical / ML packages
• Experience with AWS or other cloud computing environments, specifically within the data domain
• Experience with Git for code management
• Specific experience deploying machine learning models to production environments and the relevant experience working closely with Product, Data Engineering and Engineering Team members

Desirable

• We are looking for a talented Staff Data Scientist to lead projects related to direct-to-consumer lead generation models, credit risk analytics, fraud detection, identity verification, collections, and servicing model development
• You will be working with a versatile team of data scientists and financial professionals to design and implement models underlying the core functions of Octane's lending business and loan portfolios
• You will touch all phases of the data science lifecycle from working closely with business stakeholders to understand how data science and predictive models can help achieve core business goals and objectives, to then identifying and aggregating appropriate data assets from which to development relevant predictive machine learning models to deploying, assessing, and monitoring said models
• Apply state of the art machine learning, statistical modeling, and optimization techniques to drive business value
• Own the entire model development lifecycle from conception to deployment and model updates
• Convert insights into concrete, significant recommendations for business or product improvement, and succinctly communicate these findings to both executive and non-executive partners
• Analyze large data sets of proprietary and third-party data to derive insights and recommendations for policy, strategy, or product decisions
• Research and keep up to date with new modeling techniques and tools
• Mentor and guide junior data scientists, establish best practices for development, measurement and tracking of algorithm performance