Lead Data Scientist -Platform AI Acceleration

JPMorgan Chase & Co.; Glasgow; On-site; GBP 90,000 - 130,000; Full time

Job summary

JPMorgan Chase & Co. in the United Kingdom seeks a Lead Data Scientist and Generative Lead to design, build, and operate production-grade ML and GenAI services, while setting scalable technical direction across multiple workstreams.

You will mentor engineers, establish delivery standards for security and reliability, and stay close to code and architecture decisions to drive enterprise-ready AI solutions.

Qualifications

• Postgraduate degree in a quantitative field.
• Hands-on experience building AI-enabled production systems.
• Strong statistical and experimental design background.
• Deep expertise with modern ML/DL stacks and LLMs.
• Experience deploying models in secure, enterprise environments.

Responsibilities

• Lead design, build, and operate production-grade ML/GenAI services.
• Set engineering standards and ensure governance, security, and reliability.
• Mentor engineers and promote best practices across teams.
• Collaborate with Infrastructure Platforms AI teams on priority use cases.
• Translate business needs into scalable technical solutions.
• Develop reusable assets and reference implementations for platform users.
• Oversee evaluation, monitoring, and cost optimization of models.

Skills

Data Science

ML Engineering

LLMs

Model Deployment

Production ML

Data Analysis

Python

Cloud Platforms

Education

Masters or PhD in Data Science/CS/Math

Tools

PyTorch

TensorFlow

scikit-learn

Hugging Face Transformers

Distributed Training

Job description

The Applied Artificial Intelligence and Machine Learning (Applied AI/ML) team within Infrastructure Platforms is transforming how the firm delivers strategic infrastructure platforms-based solutions—both by applying AI/ML within engineering workflows and by building scalable AI hosting platforms and capabilities for enterprise use.

As Lead Data Scientist and Generative Lead within J.P.Morgan, you will operate as a hands-on engineering leader responsible for designing, building, and running production-grade ML and Generative AI services, while setting technical direction that scales across multiple workstreams. You will remain close to the code and architecture decisions, establish delivery and engineering standards, and ensure solutions meet enterprise expectations for security, stability, and operational rigor.

The ideal candidate brings a strong foundation insoftware engineering and AI/ML, along with proven experience leading the development and production operation of AI-enabled systems in secure, enterprise environments.

In this role, you will collaborate closely with Infrastructure Platforms AI teams to address priority use cases, design and build services, and promote best practices for scalable, resilient, and secure AI adoption. You will also mentor engineers, contribute to firmwide standards and thought leadership, and help ensure the organization stays at the forefront of AI engineering advancements.

Job Responsibilities

• Analyze large datasets to extract actionable insights and drive data-driven decision-making
• Evaluate and assist hardening of AI powered use cases on enterprise platforms, defining and applying evals and production drift monitoring, supported by automated data profiling and quality checks (leakage detection, imbalance, missingness)
• Select and apply models end-to-end across ML, deep learning, and LLM-based approaches, including training, tuning, calibration/thresholding, robustness testing, and structured error/failure-mode analysis.
• Co-Develop and implement LLM-based, machine learning models and algorithms to solve complex operational challenges.
• Ship reusable enablement assets for platform users (playbooks, templates, reference implementations) and continuously improve them using feedback loops from production telemetry and incident learnings.
• Collaborate with wider technology groups for AI driven workflows and use cases, to understand business needs and translate them into technical solutions.
• Define standards and practices to ensure regulatory and data-privacy considerations are baked into system design and implementation.

Required qualifications, capabilities, and skills

• Post Graduate qualification (Masters or PhD) Data Science, Computer Science, Mathematics.
• Building and shipping data-driven/AI-enabledproduction systems, with significant hands-on model development across statistical, classical ML, deep learning, and LLM-based approaches—covering feature/label strategy, training, evaluation, tuning, deployment, and monitoring.
• Strong grounding instatistics, probability, and experimental design, with the ability to design evaluations, interpret results, and make decisions under uncertainty.
• Deep hands-on experience with modernML/DL stacks(e.g., PyTorch and/or TensorFlow, scikit-learn, Hugging Face Transformers).
• Proven experience withdistributed training and scalable model serving, using modern architectures, tools, and frameworks.
• Hands-on experience deploying and operating models incloud production environments, including training/tuning workflows, inference operations, monitoring, and performance/cost optimization.
• Strong technical depth inLLMs/SLMs, including model selection trade-offs (latency/cost/quality), fine-tuning/adaptation where appropriate, and production serving considerations.
• Hands-on experience designing and operatingRAG systems including quality measurement and grounding controls.
• Strong technical depth inagentic AI systems, including tool/function calling, orchestration patterns, guardrails, structured outputs, and evaluation for reliability and safety.

Preferred qualifications, capabilities, and skills

• Published technical papers, patents, or significant internal publications; conference presentations (speaker/panel) on ML/GenAI/Agentic AI topics.
• Open-source contributions, including maintaining or meaningfully contributing to ML/GenAI GitHub repositories (libraries, tooling, eval harnesses, MLOps components).
• Experience with ML accelerators and performance optimization (e.g., GPUs/TPUs), including profiling, distributed training, and inference optimization.