Lending Decision Science Analyst/Associate

Job Description

CIMD Marcus by Goldman Sachs - Lending Decision Science - Analyst/Associate - BengaluruLocation(s) IN-BengaluruJob ID 2020-63363 Schedule Type Full Time Level Analyst Function(s) General, Quantitative Engineer Region India Division Consumer and Investment Management Division Business Unit Loans Data Science Employment Type Employee MORE ABOUT THIS JOB CONSUMER AND INVESTMENT MANAGEMENT DIVISION (CIMD) The Consumer and Investment Management Division includes Goldman Sachs Asset Management (GSAM), Private Wealth Management (PWM) and our Consumer business (Marcus by Goldman Sachs). We provide asset management, wealth management and banking expertise to consumers and institutions around the world. CIMD partners with various teams across the firm to help individuals and institutions navigate changing markets and take control of their financial lives.CONSUMER (MARCUS BY GOLDMAN SACHS) The firm s Consumer business, Marcus by Goldman Sachs, combines the entrepreneurial spirit of a startup with more than 150 years of experience. Today, we serve millions of customers across multiple products including lending, deposits, financial tools, and our partnership with Apple on Apple Card. We use innovative design, data, engineering and other core capabilities to provide customers with powerful tools and products that are grounded in value, transparency and simplicity. As we build a leading digital consumer bank and expand into new products and partnerships, we are looking for leaders and individual contributors to join our team. RESPONSIBILITIES AND QUALIFICATIONS Job Summary & ResponsibilitiesAs part of the decision and data science function for Marcus lending business, one should be at the forefront of a data-driven initiative to optimize decision making. This role will draw upon knowledge of programming and Quantitative solutions. This role entails:

• Rapidly prototype early-stage solutions and design / evaluate predictive models and advanced algorithms to drive business decisions throughout the customer lifecycle (prospecting, acquisition, underwriting, fraud, collections, enhancing customer experience)
• Understand the systems and the business processes that populate those systems with data
• Carry out data processing including statistical analysis, variable selection, and dimensionality reduction, custom attribute engineering, as well as the evaluation of new data sources
• Leverage methods from diverse disciplines such as traditional modeling, machine learning, deep learning, artificial intelligence, statistical modelling, information theory, information retrieval and other areas to gain customer insights, draw conclusions and work with business partners to put those insights into action
• Participate in data architecture decisions and partner with technology teams to implement models/algorithms in production
• Understand finance, banking regulation and performance metrics including ROE and ROA
• Help document your assumptions and methodologies, as well as carry out validation and testing to facilitate peer reviews and independent model validation
• Think strategically on a higher level, proposing new business metrics or suggesting alternatives, creating highly interpretive models that imply new context and new semantics for data

Basic Qualifications

• B. Tech./M. Tech., BS /MS or PhD in a quantitative field - Applied Mathematics, Physics, Engineering, Computer Science, Statistics, Econometrics and other Quantitative fields
• Quantitative background including an understanding of data science, probability and statistics
• Strong programming background in compiled or scripting languages (C/C++, Python, R, Java, etc.)
• Ability to explain complex models and analysis to diverse audience

Preferred Qualifications

• Familiarity with advanced ML models - neural networks (feed forward, CNNs, RNNs, LSTM), Hidden Markov Models, random forests, SVMs, multivariate analysis, clustering, dimensionality reduction or participation in Kaggle type data science competitions
• Experience with distributed computing (Hadoop, Spark)
• Experience in Consumer Lending, developing Underwriting, Collections or Marketing models

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Keyskills :
data processingneural networks computer scienceasset management machine learningconsumer lending wealth managementdata architect