Lead Data Scientist - Fraud

JPMorgan Chase & Co.
London
9 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Lead Data Engineer

Lead Data Analyst

Data Scientist

Lead Data Scientist - FraudJob Description

We know that people want great valuebined with an excellent experience from a bank they can trust, so we launched our digital bank, Chase UK, to revolutionise mobile banking with seamless journeys that our customers love. We're already trusted by millions in the US and we're quickly catching up in the UK - but how we do things here is a little different. We're building the bank of the future from scratch, channelling our start-up mentality every step of the way - meaning you'll have the opportunity to make a real impact.

As a fraud data scientist at JPMorgan Chase within the International Consumer Bank, you will be a part of a flat-structure organization. Your responsibilities are to deliver end-to-end cutting-edge solutions in the form of cloud-native microservices architecture applications leveraging the latest technologies and the best industry practices. You are expected to be involved in the design and architecture of the solutions while also focusing on the entire SDLC lifecycle stages.

Our fraud analytics team is at the heart of this venture, focused on getting smart ideas into the hands of our customers. We're looking for people who have a curious mindset, thrive in collaborative squads, and are passionate about new technology. By their nature, our people are also solution-oriented,mercially savvy and have a head for fintech. We work in tribes and squads that focus on specific products and projects - and depending on your strengths and interests, you'll have the opportunity to move between them.

Job spec requirements:

The Fraud & Financial crime Product function leads the 1st line of defense business for fraud & financial crime risk, including ownership of the fraud & financial crime strategy and control framework across all products and channels. Working inside a specialist fraud team to ensure transaction monitoring and controls are optimized to reduce fraud & financial crime risk whilst ensuring 1st class client experience - you will be supporting the product from an Analytics perspective.

Job responsibilities :

Responsible for development and implementation of fraud strategies/rules to effectively detect fraudulent activities Conduct analytics to support fraud product, fraud operations and fin crime to protect the financial interest of the customers and the bank Conduct analytics to support fraud operation team to improve efficiency and decision accuracy, including translation of fraud strategy into fraud operation impact Working with the 2nd line fraud risk teams to ensure models, rulesets and strategies are effective. Ensuring allpliance, audit & control frameworks are followed - using data to support the confirmation of these processes are adhered to regulation standard. Sharing best practice across JP Morgan Chase & Co. Superior written, oralmunication and presentation skills with experiencemunicating concisely and effectively with all levels of management and partners


Required qualifications, capabilities and skills
Master's degree in numeric fields or STEM related fields, such as statistics,puter science, data science, etc Knowledge of Fraud / Financial crime processes and products Team development and management experience required.
About Us

Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations,ernments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at ourpany. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable amodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an amodation.

About the Team

Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of ourpany, ensuring that we're setting our businesses, clients, customers and employees up for success. Job ID 300

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.