The Core Engineering-L2-New York-Vice President-Quantitative Engineering

Goldman Sachs•New York, NY

15h

About The Position

The Vice President, Quantitative Engineering builds and improves reusable data pipelines, data products, operational models, and production modeling workflows across databases, lakehouse and cloud platforms, Anaplan, and other enterprise systems. The role focuses on improving data quality, lineage, orchestration, and performance while strengthening existing architectures and ensuring new development accounts for production needs. The position partners with modelers from the prototype stage to turn statistical and machine-learning models into reliable, maintainable products, with appropriate testing, controls, monitoring, documentation, and operational support. It develops practical financial-planning models, especially pipeline-style models such as headcount projections, that can independently challenge business forecasts and surface assumptions, risks, and alternative outcomes. It also supports production economic-scenario generation and other dependency-heavy modeling workflows, making them scalable, observable, reproducible, and usable by downstream teams. The role selectively applies agentic development to data discovery, model workflows, controls, scenario analysis, and reporting, with evaluation and safeguards. As a central function serving multiple divisions, it sets pragmatic technical direction, understands distinct needs, manages senior stakeholders, reconciles competing priorities, and builds consensus around reusable solutions. It reviews designs and code, mentors practitioners, and maintains service-oriented partnerships with Finance, Risk, Engineering, and business teams. Candidates should combine practical modeling and data expertise with software fluency, and a record of improving real-world models and data and bringing analytical work into dependable shared production solutions.

Requirements

Bachelor’s or Master’s degree, or U.S. or foreign equivalent, 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.
Track record of owning existing operational models, maintaining and improving them for business-management use, and scaling analytical solutions for sustained production.
Experience building independent challenger models to assess business projections, test assumptions, and explain differences.
Experience building and evaluating forecasts, selecting suitable methods, diagnosing failure modes, and handling structural change, uncertainty, and unstable real-world time series.
Strong SQL, data modeling, pipeline, orchestration, data quality, and lineage skills across relational, analytical, and cloud data platforms.
Advanced Python and R, sound software-development practices, and hands-on experience taking analytical workflows into scalable cloud production.
Working knowledge of agentic systems, including tool integration, orchestration, evaluation, state or context management, and appropriate controls.
Demonstrated experience operating from a central function and delivering for multiple divisions.
Strong service orientation and stakeholder-management skills, including reconciling competing needs, mediating trade-offs, building consensus, and connecting technical decisions to shared business outcomes.

Nice To Haves

Experience with financial-planning and driver-based models.
Experience with lakehouse architectures, Spark, or Databricks.
Rust programming experience.
Experience connecting models and data to Anaplan or similar planning platforms.
Experience scaling economic-scenario generators, simulations, or other dependency-heavy calculation workflows.

Responsibilities

Build reusable data pipelines and data products across databases, lakehouse and cloud platforms, Anaplan, and other enterprise systems.
Improve data quality, lineage, orchestration, and performance.
Maintain and improve existing operational models used for business management; strengthen existing architectures and ensure new development considers production needs.
Partner with modelers from prototype onward to deliver reliable, maintainable statistical and machine-learning products with testing, controls, monitoring, documentation, and operational support.
Develop practical financial-planning models, particularly pipeline-style models such as headcount projections, to independently challenge business forecasts and identify assumptions, risks, and alternative outcomes.
Support production economic-scenario generation and other DAG-heavy modeling workflows, making calculation graphs scalable, observable, reproducible, and accessible to downstream teams.
Apply agentic development selectively to data discovery, model workflows, controls, scenario analysis, and reporting, with appropriate evaluation and safeguards.
Set pragmatic technical direction and represent a central function across multiple divisions.
Understand divisional needs, manage senior stakeholders, mediate competing priorities, and build consensus around reusable solutions.
Review designs and code, mentor practitioners, and maintain responsive, service-oriented partnerships with Finance, Risk, Engineering, and business teams.