Data Governance Associate - BFS

Job Summary & Responsibilities

As part of the decision and data science function, 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

- Familiar with internal systems and processes or an ability to quickly learn the same

- Understand business and can frame requirements for accuracy and completeness of data) who understands the data and business well and can advise firmwide teams on the changes/additions to business/data on an ongoing basis.

Basic Qualifications

- MS or PhD in a quantitative field - Applied Mathematics, Statistics, 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