• Data Governance Engineer - Marcus by Goldman Sachs Engineering - CIMD

    Location(s) US-NY-New York
    Job ID
    Schedule Type
    Full Time
    Analyst, Associate
    Engineering, Technology
    Business Unit
    DigFin Project Data
    Employment Type

    Consumer and Investment Management (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, externally known as Marcus by Goldman Sachs, is comprised of the firm’s digitally-led consumer businesses, which include our deposits and lending businesses, as well as our personal financial management app, Clarity Money. Consumer combines the strength and heritage of a 150-year-old financial institution with the agility and entrepreneurial spirit of a tech start-up. Through the use of machine learning and intuitive design, we provide customers with powerful tools that are grounded in value, transparency and simplicity to help them make smarter decisions about their money.


    Job Summary & Responsibilities

    As part of the decision and data science function for Marcus, you will be at the forefront of a data-driven initiative to optimize decision making. This role will draw upon your knowledge of programming and mathematics applied in data governance, data quality and data anomaly detection. In this role you will:

    • Rapidly prototype early-stage solutions and design / evaluate good quality data for business decisions throughout the customer lifecycle (prospecting, acquisition, underwriting, fraud, collections, enhancing customer experience, compliance etc.)
    • Understand and document the business systems, data flows and the business architecture / processes that consume data
    • Design and execute software code to validate quality of data e.g. completeness (reconciliation between two data repository or databases) and accuracy (validity and soundness) of data intended for business decision making
    • Leverage methods from diverse disciplines such as machine learning, deep learning, statistical modelling, information retrieval and other areas to improve quality of data to gain accurate customer insights and improve business decision making
    • Participate in data architecture decisions and partner with technology/engineering teams to implement models/algorithms/solutions in production
    • Work with business partners to document data dictionary, assess risks, design data controls and review the results for data quality and anomalies
    • Think strategically on a higher level, proposing new metrics or suggesting alternatives, creating highly interpretive information that imply new context and new semantics for data


    Basic Qualifications

    • BS/MS or PhD in a quantitative field - Applied Mathematics, Physics, Engineering, Computer Science, Management Information System
    • Quantitative background including an understanding of probability, statistics
    • Strong programming background in compiled or scripting languages (C/C++, Python, Java, Scala, SQL etc.)
    • Experience working with big data technologies such as HDFS, Hadoop, Hbase, Hive, Spark etc.
    • Ability to explain clearly complex analysis to diverse audience



    Preferred Qualifications

    • Experience in data science, advanced statistics and engineering functions
    • Familiarity with statistical computing languages or packages (R, Matlab, Python, numpy/scikit-learn, Tensorflow, Keras, Pytorch etc.) 
    • Familiarity with advanced ML models – anomaly detection, neural networks (feed forward, CNNs, RNNs, LSTM), Hidden Markov Models, random forests, SVMs, multivariate analysis, clustering, dimensionality reduction etc.
    • Experience with distributed computing (Hadoop, Spark)
    • Experience in a start-up business or a new business line within a larger organization



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