Analyst-Data Science

Useful ML Systems Knowledge

• Understanding of model serving and inference, or motivation to learn these areas.
• Useful concepts include vLLM or similar runtimes, continuous batching, KV caching, quantization, throughput and latency trade-offs, GPU memory, mixed precision, device placement, profiling, and structured generation.
• CUDA, Triton, distributed systems, Kubernetes, NCCL, and low-level optimization are useful but not required.

Evidence of Technical Depth

• Relevant evidence may include a substantial ML or systems project, research or thesis work, paper reproduction, open-source contribution, inference or training system, serious side project, benchmark, or measured performance experiment.
• Candidates should be able to explain what they built, why they made design choices, what they measured, what failed, and what they learned.

Academic Background

• A strong foundation in Computer Science, Mathematics, Statistics, Engineering, Physics, Operations Research, or a related quantitative discipline is preferred.
• Research experience is useful but not mandatory.

About American Express

Benefits and Flexibility

• Benefits may include base salary, bonus incentives, retirement and financial-well-being support, insurance, parental leave, wellness centers, counseling, and career development.
• Working arrangements may be hybrid, onsite, or virtual depending on the role and business need.

Employment Conditions

• Employment is conditioned upon successful completion of a background verification check, subject to applicable laws and regulations.