Applied AI ML Senior Associate

JPMorgan Chase & Co.; Greater London; On-site; GBP 60,000 - 80,000; Full time

Job summary

A major international bank is seeking a Senior Associate in the ICB Risk Modelling team in Greater London. This role involves developing advanced machine learning models to tackle fraud and credit risk issues. Candidates should have a strong programming background, especially in Python, and experience with machine learning techniques. Ideal applicants will hold an advanced degree in a quantitative field and possess excellent communication skills to work collaboratively with technical and business teams.

Qualifications

• At least 4 years of experience in applied data science or software engineering.
• Experience in building and deploying ML models on AWS.
• Solid understanding of machine learning and deep learning methods.

Responsibilities

• Develop state-of-the-art machine learning models for risk problems.
• Assist in defining problem statements and execution roadmaps.
• Collaborate to devise AI solutions and operationalize ML models.

Skills

Python programming skills

Machine learning techniques

Deep learning methods

Data science experience

Big data experience

Communication skills

Team player

Education

Advanced degree (MSc or PhD) in a quantitative or technical discipline

Tools

NumPy

Scikit-Learn

Pandas

Hugging Face

TensorFlow

PyTorch

AWS (SageMaker, EC2, Glue)

Job description

The ICB (International Consumer Banking) business within JPMorgan Chase has grown significantly since its launch in 2021, and we expect the business to expand further over the next years. Join the expansion of the Chase digital bank across the UK and Europe and help us continue to build our award-winning bank.

The ICB Risk Modelling team is responsible for developing statistical and machine learning models to reduce fraud and credit risk within ICB. The team also engages with external vendors supporting onboarding vendor models including working through Model Governance to obtain appropriate approvals for different uses. The team executes and prepares model surveillance while providing insights for various regulatory requirements.

As a Senior Associate in the ICB Risk Modelling team, you will play a crucial role in analysing business problems, experimenting with state-of-the-art algorithms, and developing machine learning and deep learning models. You will be part of an innovative team, working closely with our product owners, risk officers, data engineers, and software engineers to build new models and systems. We are looking for someone with a passion for programming, data, statistics, and ML, who can understand the strategic demands and data landscape in large and complex organisations.

Job Responsibilities

• Develop state-of-the-art machine learning models to solve real-world risk problems and enhance automation and decision-making processes.
• Assist product leadership in defining the problem statements and execution roadmap.
• Collaborate with business, operations, and other technology colleagues to understand AI needs and devise possible solutions.
• Develop end-to-end ML pipelines and operationalise the end-to-end orchestration of the ML models to support various use cases such as credit risk management, portfolio monitoring, fraud risk prevention, and customer profiling.
• Build both batch and real-time model prediction pipelines with existing applications and front-end integrations.
• Collaborate to develop large-scale data modelling experiments and explain complex concepts to senior stakeholders.
• Work with multiple partner teams, such as Strategy, Technology, Product Management, Legal, Compliance, and Business Management, to deploy solutions that meet the firm’s high governance standards.

Required Qualifications, Capabilities, and Skills

• Advanced degree (MSc or PhD) in a quantitative or technical discipline, or significant practical experience in the industry.
• Proficient Python programming skills with hands‑on experience in writing production‑quality code.
• At least 4 years of experience in applied data science, ML techniques, or software engineering.
• Solid understanding of machine learning and deep learning methods, and familiarity with large language models.
• Extensive experience with machine learning and deep learning toolkits (e.g. NumPy, Scikit‑Learn, Pandas, Hugging Face, TensorFlow, PyTorch, etc.).
• Experience in building and deploying ML models on AWS, especially using AWS tools like SageMaker, EC2, Glue, etc.
• Experience with Big Data and scalable model training, along with solid written and spoken communication skills to effectively communicate technical concepts and results to both technical and business audiences.
• Excellent communication skills and a team player.

Preferred Qualifications, Capabilities, and Skills

• Experience with ML model explainability.
• Knowledge of credit scorecards and credit risk management is a plus.