Data Scientist, Kiavi

• Bachelor's degree in a quantitative discipline (e.g., statistics, economics, finance, physics, mathematics) and 2+ years of work experience in a modeling group at a commercial bank, investment bank, consulting firm, or fintech company
• OR
• Advanced degree (PhD or MS) in a quantitative discipline
• Statistical skills especially in model development at an account, customer, or asset level
• Solid understanding of machine learning techniques and AI capabilities
• Strong programming skills in R, SQL, Python
• Team player with strong interpersonal skills
• Excellent oral and written communication skills with the ability to explain technical concepts in practical terms
• Experience with Monte Carlo simulation and/or Bayesian statistics

Desirable

• Our Risk Analytics team is made up of 360-degree thinkers that analyze complex problems and come up with creative solutions
• We love to combine advanced analytics and machine learning skills with deep domain knowledge about the business when creating solutions
• You will work with some of the most fascinating data sets in the world, alongside a world-class group of product engineers, sales and marketing leaders, and business operators
• Kiavi’s Risk Analytics team is seeking a talented Data Scientist to join us in our effort to reinvent real estate investment lending
• You will be working with the Director of Risk Analytics to tackle fun business problems related to credit risk management, asset valuation, process optimization, and more. An ideal candidate should be able to utilize advanced data manipulation and modeling techniques to work on structured and unstructured data to solve complex business problems
• You’ll also have interaction with MLOps engineers, Product Managers, and Capital Markets subject matter experts to help drive select initiatives. If you are keen on joining a dynamic company and working on intellectually challenging problems in a fast paced environment, please reach out to us
• Develop and maintain innovative models for asset valuation. This requires thorough statistical analysis of the underlying data and understanding of the various macroeconomic factors and risk factors that impact prices and liquidity in the real estate market
• Develop models for portfolio management purposes such as value forecasting or market risk segmentation
• Closely provide regular ongoing model performance assessments and enhance existing models
• Develop analytical tools to support other teams
• Working with the technology team, drive initiatives to enhance data capabilities and streamline infrastructure
• Work closely with engineers/data infrastructure team in designing useful data attributes
• Perform exploratory analysis and make recommendations on improving current data models, decisioning frameworks, etc
• Refactor existing analytic solutions to improve scalability

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