Head of Quantitative Engineering & Forecasting

New York Times; New York (NY); On-site; USD 191,000 - 237,000; Full time

Job summary

Goldman Sachs is seeking a Vice President, Quantitative Engineering in New York to lead the design, development, implementation, and documentation of advanced quantitative models and scenarios for time series forecasting.

You will integrate economic, financial, and business-risk variables to address practical issues in finance and risk management and conduct uncertainty quantification through simulation and modern methods.

Qualifications

• PhD or Master’s or Bachelor’s in a quantitative field with required experience.
• High proficiency in statistical methods, ML, and time-series analysis.
• Experience with production-grade cloud deployment and data management.

Responsibilities

• Lead the design, development, and documentation of advanced quantitative models and scenarios for time series forecasting.
• Incorporate economic, financial, and business-risk variables to address practical finance issues.
• Perform uncertainty quantification using Monte Carlo and conformal prediction methods.
• Publish model risk documentation to support independent validation.

Skills

C++

Python

R

Econometrics

Time-series analysis

Explainable ML

Cloud deployment

Data management

Model validation

Education

PhD in Mathematics, Computer Science, Financial Engineering, Applied Mathematics or Statistics

Master’s in Mathematics, Computer Science, Financial Engineering, Applied Mathematics or Statistics

Bachelor’s in Mathematics, Computer Science, Financial Engineering, Applied Mathematics or Statistics

Tools

SQL

Spark

Hadoop

Job description

Goldman Sachs is seeking a Vice President, Quantitative Engineering in New York to lead the design, development, implementation, and documentation of advanced quantitative models and scenarios for time series forecasting.

You will integrate economic, financial, and business-risk variables to address practical issues in finance and risk management and conduct uncertainty quantification through simulation and modern methods.