VP AI Research — Time Series & Deep Learning Lead

The Goldman Sachs Group; New York (NY); On-site; USD 150,000 - 300,000; Full time

Job summary

Goldman Sachs is seeking a VP-level AI Researcher to design, train, and evaluate deep learning models for financial time series in New York. The role combines hands-on research with collaboration across quant and engineering teams to productionize models.

You will own end-to-end research, from data curation to model evaluation in large GPU clusters, with a focus on scalable, robust methods and rigorous experimentation.

Qualifications

• Bachelor’s, Master’s, or Ph.D. in Computer Science, ML, Statistics, Mathematics, Physics, EE, Quant Finance, or related field.
• Minimum 7 years of industry experience building, training, deploying deep learning models, with sequential/time series focus.
• Deep expertise in CNNs/TCNs, Transformers, autoencoders, GANs, diffusion models, GNNs, Bayesian methods, RL.

Responsibilities

• Design, implement, and train modern DL architectures for forecasting financial time series.
• Lead end-to-end research from framing questions to production-ready models.
• Partner with quantitative researchers and engineers to translate research into assets and backtests.
• Mentor junior researchers and maintain rigorous evaluation and reporting.

Skills

Python

PyTorch

TensorFlow

JAX/Flax

Distributed training

Time-series models

Research leadership

Education

Bachelor/Master/PhD in CS/ML/Quantitative fields

Tools

ONNX

AWS

Docker

Kubernetes

Job description

Goldman Sachs is seeking a VP-level AI Researcher to design, train, and evaluate deep learning models for financial time series in New York. The role combines hands-on research with collaboration across quant and engineering teams to productionize models.

You will own end-to-end research, from data curation to model evaluation in large GPU clusters, with a focus on scalable, robust methods and rigorous experimentation.