AI Research Associate: Financial Time Series & DL

Goldman Sachs (lateral); New York (NY); Presencial; USD 180.000 - 240.000; Jornada completa

Descripción de la vacante

Goldman Sachs in New York, NY seeks a deeply hands-on AI Research Associate to lead the design, training, and evaluation of deep learning models for financial time series. This individual contributor role covers framing questions, curating data, designing architectures, running large-scale experiments, and partnering with quant and engineering teams to productionize models.

You will contribute to a fast-moving research space, bringing rigorous evaluation as well as generalizable abstractions

Responsabilidades

• Design, implement, and train modern deep learning architectures for forecasting, representation learning, and generative modelling of financial time series, including CNNs, Transformers, and related models.
• Build and benchmark time-series specialized architectures such as WaveNet, N-BEATS/HiTS, DeepAR, PatchTST, and compare to classical econometric methods like ARIMA and GARCH.
• Train at scale across GPU clusters and cloud with distributed data parallel, fully sharded data parallel, mixed precision, and hyperparameter search; optimize inference paths.
• Develop robust evaluation frameworks for finance data (walk-forward, cross-validation, regime analysis, uncertainty quantification, and data snooping controls).
• Collaborate with quantitative researchers and engineers to translate model outputs into alpha signals, portfolio ideas, and production-ready tooling.
• Contribute reusable models, datasets, benchmarks, and tooling to a shared research platform; mentor junior researchers and engineers.

Descripción del empleo

Goldman Sachs in New York, NY seeks a deeply hands-on AI Research Associate to lead the design, training, and evaluation of deep learning models for financial time series. This individual contributor role covers framing questions, curating data, designing architectures, running large-scale experiments, and partnering with quant and engineering teams to productionize models.

You will contribute to a fast-moving research space, bringing rigorous evaluation as well as generalizable abstractions