Applied AI & Machine Learning Scientist - Senior Associate

JPMorgan Chase & Co.; Greater London; On-site; GBP 70,000 - 90,000; Full time

Job summary

A leading financial institution in Greater London is seeking a Machine Learning Scientist to join their elite AI team. In this role, you will develop and deploy advanced machine learning models that are pivotal in transforming business operations, particularly in Cybersecurity and Technology Infrastructure. Ideal candidates will have a master's degree in a relevant field or a PhD with experience in machine learning. Strong familiarity with deep learning frameworks and excellent communication skills are required. This position offers the chance to work on cutting-edge projects alongside industry experts.

Qualifications

• Masters degree in a related discipline or PhD with relevant experience.
• Extensive experience with machine learning and deep learning toolkits.
• Ability to communicate technical concepts effectively.

Responsibilities

• Research and explore new machine learning methods.
• Develop machine learning models to solve business-critical issues.
• Collaborate to deploy solutions into production.

Skills

Machine Learning

Deep Learning

TensorFlow

PyTorch

Data Analysis

Statistical Analysis

Education

Masters in Computer Science or related field

PhD in related field

Tools

NumPy

Scikit-Learn

Pandas

LangChain

LangGraph

Job description

Job Description

The Applied Innovation of AI (AI2) team is an elite machine learning group located within the Tech CDO at JP Morgan Chase. Strategically positioned in the Chief Technology Office, our work spans across Cybersecurity, Global Technology Infrastructure and the Software Development Lifecycle (SDLC). With this unparalleled access to technology groups in the firm, the role offers a unique opportunity to explore novel and complex challenges that could profoundly transform how the bank operates.

As a Machine Learning Scientist, you will apply sophisticated machine learning methods to a wide variety of complex tasks including data mining and exploratory data analysis and visualization, text understanding and embedding, anomaly detection in time series and log data, large language models (LLMs) and generative AI for technology use‑cases, reinforcement learning and recommendation systems. You must excel in working in a highly collaborative environment together with the business, technologists and control partners to deploy solutions into production. You must also have a passion for machine learning and invest independent time towards learning, researching and experimenting with new innovations in the field. You must have solid expertise in Deep Learning with hands‑on implementation experience and possess strong analytical thinking, a deep desire to learn and be highly motivated.

Job Responsibilities

• Research and explore new machine learning methods through independent study, attending industry‑leading conferences and experimentation
• Develop state‑of‑the‑art machine learning models to solve real‑world problems and apply them to complex business‑critical problems in Cybersecurity, Software and Technology Infrastructure
• Collaborate with multiple partner teams in Cybersecurity, Software and Technology Infrastructure to deploy solutions into production
• Drive firmwide initiatives by developing large‑scale frameworks to accelerate the application of machine learning models across different areas of the business
• Contribute to reusable code and components that are shared internally and also externally

Required qualifications, capabilities and skills

• Masters in a related discipline (e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science) with 2 years experience or PhD with 1 year of industry or research experience in the field.
• Hands‑on experience and solid understanding of machine learning and deep learning methods
• Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit‑Learn, Pandas)
• Extensive experience with large language models (LLMs) and accompanying tools & techniques in the LLM ecosystem (e.g. LangChain, LangGraph, Vector databases, opensource Models, RAG, Agentic Systems & Workflows, LLM fine‑tuning)
• Scientific thinking and the ability to invent
• Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
• Experience with big data and scalable model training
• Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences
• Curious, hardworking and detail‑oriented, and motivated by complex analytical problems
• Ability to work both independently and in highly collaborative team environments

Preferred qualifications, capabilities and skills

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Strong background in Mathematics and Statistics

In‑depth knowledge and proficiency in agentic frameworks like LangChain and LangGraph and related platforms like LangSmith.

Familiarity with the financial services and related technologies and industries, including familiarity in networking and infrastructure platforms.

Experience with A/B experimentation and data/metric‑driven product development

Experience with cloud‑native deployment in a large scale distributed environment

Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal

Ability to develop and debug production‑quality code

Familiarity with continuous integration models and unit test development