Associate Applied AI - Deep Learning/Time Series

ABOUT GOLDMAN SACHS:

Qualifications

• Required
• A Bachelor's, Master's, or Ph.D. degree in Computer Science, Machine Learning, Statistics, Mathematics, Physics, Electrical Engineering, Quantitative Finance, or a related quantitative discipline.
• A minimum of 3+ years of industry experience building, training, and deploying deep learning models, with a substantial portion focused on sequential or time series data. Candidates with a Ph.D. and fewer years of industry experience will be considered where the depth of research experience is demonstrably equivalent.
• Deep expertise across modern deep learning architectures: CNNs and TCNs, Transformers, autoencoders, GANs, diffusion models, GNNs, Bayesian methods, and reinforcement learning.
• Strong working knowledge of classical time series and econometric modelling — ARIMA, GARCH, Kalman filtering, state space models — and clear judgment on when deep learning does and does not beat them.
• Practical experience with modern time series architectures such as WaveNet, N-BEATS / N-HiTS, DeepAR, PatchTST, or Time Series Foundation Models.
• Expert-level Python and deep proficiency in PyTorch, TensorFlow / Keras and/or JAX / Flax.
• Demonstrated experience with distributed and accelerated training (DDP, FSDP, multi-node GPU training) and model export and optimization workflows including ONNX.
• Rigorous foundations in statistics, probability, stochastic processes, optimization, and signal processing.
• Excellent oral and written communication skills, with the ability to articulate research trade-offs to both PhD researchers and non-technical business stakeholders.
• Comfort operating in a quickly evolving environment with a high degree of ambiguity and rapid change.
• Publications at top-tier venues such as NeurIPS, ICML, ICLR, AISTATS, or KDD, or in leading quantitative finance journals.
• Prior machine learning or data science experience at a hedge fund, asset manager, proprietary trading firm, or systematic trading desk.
• Experience across multiple asset classes — equities, fixed income, FX, commodities, or multi-asset.
• Familiarity with Bayesian deep learning, probabilistic programming, or conformal prediction for uncertainty quantification.
• Experience with cloud platforms and containerized training environments (AWS, Kubernetes, Docker).
• Exposure to model risk management or regulatory frameworks in financial services.
• Contributions to open-source machine learning or time series libraries.
• Work on genuinely hard research problems with immediate, measurable commercial impact across the firm.
• Access to proprietary, high-quality cross-asset financial datasets at a scale few institutions can offer, and GPU compute to model them properly.
• Join a standalone research group that values rigor — strong baselines, rigorous evaluation, and reproducibility.
• Freedom to publish and engage with the broader research community.
• Direct partnership with quantitative researchers and strategists across multiple desks who will use what you build.
• Develop deep expertise at the intersection of frontier deep learning and quantitative finance, with the scope to shape the firm's applied AI research agenda.
• Salary Range

Preferred Qualifications

• Preferred
• The expected base salary for this New York, New York, United States-based position is $100,000-$200,0000. In addition, you may be eligible for a discretionary bonus if you are an active employee as of fiscal year-end.