Vice President with Goldman Sachs Bank USA in New York, New York.
Work Schedule: 40 hours per week (9:00 a.m. to 6:00 p.m.)
Duties: Vice President role with Goldman Sachs Bank USA in New York, New York within the Marcus Personal Finance Management Data Science group, responsible for: utilizing data science techniques combined with intuitive design to provide our customers with powerful insights that help them make smarter decisions with their money; designing and engineering robust algorithms and scalable application software architecture for use with the Firm’s Marcus deposit, lending, and personal finance management mobile application, as well as deploying and maintaining the Firm’s application infrastructure to optimize operational efficiency; architecting massive reservoirs for big data and ensuring that our data pipeline provides clean, organized, and aggregated data into databases that can be utilized by Data Science algorithms to generate personal finance insights-these insights are used within the personal finance management application and presented to our customers; developing, constructing, testing and maintaining architectures, such as databases and large-scale data processing systems; performing Extracting, Transforming and Loading (ETL) of very large datasets (terabyte scale) from multiple sources using tools such as Hadoop or Hive; and ensuring customer data is securely stored and mining and analyzing the data to create personalized actionable insights that are delivered to our customers, within the closed loop PFM App, in order to help them save money.
Job Requirements: Bachelor’s degree (U.S. or foreign equivalent) in Computer Science, Statistics, Data Science or a related field. Four (4) years of experience in the job offered or a related data engineer/scientist position. Prior work experience must include four (4) years with analyzing large data sets as a data engineer or data scientist. Prior work experience must include two (2) years with: building, scaling, and maintaining data pipeline and systems; developing infrastructure using relational databases and distributed systems including SQL and NoSQL queries, database definition, and schema design; utilizing fundamental computing concepts, including data structures and algorithms, architecture, and software engineering, to deliver high performance production quality code; engineering and programming data structures; writing high performance production quality code with Python; and developing data architecture and Extracting, Transferring and Loading (ETL) for very large (terabyte scale) datasets using Hadoop, Hive, SQL or similar technologies.
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