VP, Internal Audit Data Analytics & Automation

Goldman Sachs; New York (NY); On-site; USD 140,000 - 220,000; Full time

Job summary

Goldman Sachs Internal Audit is seeking a strong data scientist to join the Embed Data Analytics team. You will work with audit teams to build reusable analytical tools that improve efficiency and provide insights into the firm’s control environment.

The role focuses on data modeling, ETL, and applying stats and ML techniques to assess risks, test controls, and support audit testing across global businesses.

Qualifications

• 8+ years of experience in data analytics or data science.
• Bachelor’s degree in CS, Math, or Statistics.
• Strong experience in RDBMS/SQL.
• Experience with ETL processes and data governance.
• Proficiency in programming languages, such as Python, Java, or C++.
• Knowledge of statistics, including time series, correlation and regression.
• Ability to communicate findings clearly to audit teams.

Responsibilities

• Perform Data Modeling, Data Engineering, Data Governance, and maintenance of Entitlements.
• Obtain/Manage requirements for each audit project and provide insights for sampling and data integrity.
• Build production-ready analytical tools to automate repeatable processes using Tableau, Spotfire or QlikView.
• Execute ETL tasks: extraction, transformation, and loading of data.
• Write data analysis code (Python, Java, or Slang).
• Identify automation opportunities to improve audit processes and data analytics.
• Collaborate with audit teams to identify risks and data sourcing needs.
• Build and maintain relationships with Audit team members.

Skills

SQL

Python

Java

Data governance

ETL

Data analysis

Statistics

Communication

Machine learning

Education

Bachelor's degree in CS/Math/Statistics

Tools

Tableau

Spotfire

QlikView

SAS

SPSS

R

Job description

Goldman Sachs Internal Audit is seeking a strong data scientist to join the Embed Data Analytics team. You will work with audit teams to build reusable analytical tools that improve efficiency and provide insights into the firm’s control environment.

The role focuses on data modeling, ETL, and applying stats and ML techniques to assess risks, test controls, and support audit testing across global businesses.