below marketmost similar roles pay hereabove market
This role pays more than
70%
of similar roles. Most pay
$161,250–$213,696
— the blue band above.
At the midpoint, this role pays about
$195k
versus about
$187k
for comparable roles.
Based on 240 similar postings.
Employer
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
71 open roles
on FindRole.
Listed pay typically runs
$140,000–$250,000
across 34 roles with salary data.
The Core Engineering, New York, Vice President, Quantitative Engineering joins the Core Engineering team to build reusable data pipelines and products across databases, lakehouse, and cloud platforms. This role involves maintaining operational models for business management, improving data quality and lineage, and partnering with modelers to transform complex statistical and machine learning models into reliable production products. Key responsibilities include developing practical models for financial planning, supporting production economic scenario generation, and applying agentic development for data discovery. The candidate must possess advanced skills in Python, R, SQL, and Spark, with experience in Databricks, Anaplan, and Rust. The work focuses on solving practical numerical problems, handling messy data, and scaling analytical workflows for forecasting, planning, and scenario design across multiple divisions to identify key business risks and assumptions.
Build reusable data pipelines and products across databases, lakehouse, and cloud platforms.
Improve data quality, lineage, orchestration, and performance for enterprise systems.
Convert complex statistical and machine learning models into reliable, maintainable production products.
Develop practical financial planning models and independent challenger models to test business forecasts.
Support production economic scenario generation and scalable, observable calculation graphs.
Apply agentic development to improve data discovery, model workflows, and scenario analysis.
Set technical direction and build consensus on reusable solutions across multiple divisions.
Review designs and code while mentoring practitioners on software-enabled analytical products.
What we're looking for
Bachelor’s or Master’s degree in a quantitative or technical field such as Statistics, Electrical Engineering, Computer Science, Applied Mathematics, Economics, Physics, or Engineering.
Eight or more years of relevant experience spanning data, modeling, forecasting, financial-planning technology, or software-enabled analytical products.
Proven track record of owning, maintaining, and scaling operational models and analytical solutions for sustained production.
Strong quantitative judgment and command of practical modeling techniques for messy data, including building independent challenger models.
Experience building and evaluating forecasts, selecting fit-for-purpose methods, and handling structural change and uncertainty in real-world series.
Strong SQL, data-modeling, pipeline, orchestration, quality, and lineage skills across relational, analytical, and cloud data platforms.
Advanced Python and R skills with sound software-development practices and experience taking analytical workflows into scalable cloud production.
Experience with lakehouse architectures, Spark, Databricks, Rust, or connecting models to Anaplan (preferred).