Staff AI Scientist, Fintech Innovation

Overview

Join an amazing team of AI scientists, accountable for creating AI solutions in the fintech industry, who assist in evaluating the risk of lending products.

Responsibilities

• Provides guidance and support leadership to business leaders and stakeholders on how best to harness available data in support of critical business needs and goals.
• Leads the full cycle of iterative big data exploration, including hypothesis formulation, algorithm development, data cleansing, testing, insight generation/visualization, and action planning.
• Uses considerable expertise and independent judgment in collaborating with peers, data engineers, database managers, and business analysts in designing and implementing the research strategy needed to methodically and iteratively structure, extract, cleanse, sample, test, validate, and communicate data-driven insights from complex sources and significant volumes of data for complex and unique business problems
• Applies proven methods and hacking skills in working with divergent data types, data scales, and big data (petabytes), to explore and extrapolate data-driven insights using advanced, predictive statistical modeling and testing applied to data acquired and cleansed from a range of sources (relational and non-relational NoSQL databases)
• Provides to business stakeholders the entrepreneurial guidance essential for appropriately interpreting and building on findings, and fully exploiting the insights revealed through the research.
  
  

• Advanced degree in Statistics, Applied Mathematics, Computer Science, or a related field (or equivalent experience)
• 5+ years of experience in developing predictive models and statistical analysis in a financial services or risk management‌
• Strong proficiency in programming languages such as Python, R, or Scala, and experience with data manipulation and visualization tools
• Expertise in statistical modeling techniques such as GLM, logistic regression, decision trees, time-series forecasting, and machine learning algorithms
• Experience in Generative AI techniques, including autoencoders, GANs, VAEs
• Practical experience with ETL processes, data warehousing, and cloud-based computing platforms
• Excellent communication skills, including the ability to present complex data and insights to non-technical business stakeholders
• Strong analytical and problem-solving skills, with a customer-centric approach
• Expertise and experience in Supervised and Unsupervised ML techniques
• Experience with graph data and insight extraction
• Expertise with Spark for data manipulation, and ML feature creation