M&A Quant Advisory, Vice President

Goldman Sachs

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Work type: On-site
Location: New York, NY
Posted: 1 day ago
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $177k

$126k most similar roles pay here $228k

This listing doesn't post a salary. Most similar roles pay $135,500–$218,500.

Based on 240 similar postings.

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About Goldman Sachs

Goldman Sachs is a leading global investment banking, securities, and investment management firm providing financial services to corporations, financial institutions, governments, and individuals.

Goldman Sachs currently has 187 open roles on FindRole.

Listed pay typically runs $130,000–$250,000 across 60 roles with salary data.

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At a glance

TL;DR · M&A Quant Advisory, Vice President

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As a Vice President in the M&A Quant Advisory team at Goldman Sachs, you will collaborate closely with Investment Bankers to develop quantitative models and technologies that solve complex business problems. Your day-to-day responsibilities include creating actionable insights for clients through deal performance evaluation and competitive landscape analysis, implementing advanced analytical methodologies, and building robust data pipelines using Python and machine learning techniques. You will also maintain high-quality datasets from multiple vendors and communicate findings effectively to non-technical audiences. The role requires a strong background in quantitative modeling, programming skills in Python or Java, and experience with SQL and NoSQL databases. Ideal candidates have a deep understanding of financial markets and M&A processes, along with the ability to work independently while fostering collaboration across global teams.

Skills

Python SQL MongoDB NoSQL Java CI/CD AWS Kubernetes Docker PostgreSQL Machine_Learning Statistical_Analysis Data_Pipelines Data_Storage_Solutions

What you'll do

Develop actionable insights for clients through deal performance evaluation and competitive landscape analysis.
Design advanced analytical methodologies to identify new transaction opportunities.
Implement machine learning techniques and build Python codebases for financial datasets.
Maintain high-quality datasets by designing robust data pipelines and storage solutions.
Communicate complex analyses clearly to non-technical banking clients and teams.

What we're looking for

Bachelor’s or advanced degree in a quantitative/STEM field.
Strong quantitative reasoning and problem-solving skills.
Proficiency in Python for developing analytical methodologies.
Experience with SQL, MongoDB, and NoSQL for data management.
Ability to communicate complex analyses to non-technical clients.
5+ years of experience in finance, investment banking, or capital markets.
Results-oriented work ethic with responsibility and enthusiasm.

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