Lead Data Scientist -Platform AI Acceleration

JPMorgan Chase & Co.; Glasgow; On-site; GBP 120,000 - 170,000; 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 AI services within Infrastructure Platforms.

You will set technical direction, stay close to code and architecture decisions, and mentor engineers while ensuring security, stability, and operational rigor across multiple workstreams.

Qualifications

• Post Graduate qualification (Masters or PhD) Data Science, Computer Science, Mathematics.
• Hands-on development across statistical, classical ML, deep learning, and LLM-based approaches.
• Strong grounding in statistics, probability, and experimental design.
• Experience with modern ML/DL stacks and model serving at scale.
• Production deployment, monitoring, and cost optimization of models in cloud environments.
• Expertise in LLMs/SLMs, including fine-tuning and production serving considerations.
• Experience designing and operating RAG systems with grounding controls.

Responsibilities

• Analyze large datasets to extract actionable insights and drive data-driven decisions.
• Evaluate AI-enabled use cases on enterprise platforms and monitor production drift.
• Select and apply models end-to-end across ML, DL, and LLM approaches.
• Co-develop LLM-based models and algorithms to solve operational challenges.
• Ship reusable enablement assets and improve them from production telemetry and incidents.
• Collaborate with wider tech groups to translate business needs into technical solutions.
• Define standards for regulatory and data-privacy considerations in system design.

Skills

ML/DL stacks

Distributed training

Model deployment

LLMs/SLMs

RAG systems design

Tool/agent orchestration

Evaluation & monitoring

Education

Postgraduate qualification (Masters or PhD) in Data Science/CS/Math

Tools

PyTorch/TF

scikit-learn

Hugging Face Transformers

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.