Senior Associate Data Analyst - Global Investment Bank Digital (GIBD)

JPMorgan Chase & Co.
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Analyst

Data Analyst (Permanent/FTC)

AWS Data Engineer | Senior Consultant

Data Scientist – Advanced Analytics

Data Scientist – Advanced Analytics

Data Scientist – Advanced Analytics

This job is brought to you by Jobs/Redefined, the UK's leading over-50s age inclusive jobs board.

Job Description

Uncover Actionable Insights to Drive Investment Banking Innovation.

Are you ready to revolutionize investment banking with data-driven insights? Join the Global Investment Bank Digital (GIBD) team at JPMorgan Chase, where you'll work in a fast-paced, dynamic environment to directly contribute to deal-making and client success. Collaborate with investment bankers, data scientists, and engineers to redefine deal flow and support with AI-powered tools.

As a key member of our innovation hub, you will analyze diverse datasets to uncover insights that drive investment banking success. You will rapidly prototype data-driven solutions and present insights in impactful ways to support active deals and client strategies. Your role involves collaborating with bankers to design tailored AI solutions and partnering with AI/ML teams to scale your innovations.

Job Responsibilities:

  • Deliver actionable insights by analyzing datasets to support deals and client discussions.
  • Build quick, ad hoc Minimum Viable Products (MVPs) to demonstrate data-driven solutions.
  • Conduct contextual analysis of financial and market data within investment banking workflows.
  • Present insights clearly and visually to facilitate decision-making and deal strategies.
  • Collaborate with bankers to design data analytics and AI solutions for specific needs.
  • Partner with AI/ML teams to translate insights and prototypes into scalable solutions.

Required Qualifications, Capabilities, and Skills:

  • Proficiency in Python and data analytics libraries (e.g., Pandas, NumPy, Matplotlib, Seaborn, PySpark).
  • Strong skills in SQL and experience with relational databases.
  • Experience with data visualization tools such as Tableau or Power BI.
  • Understanding of data transformation, ETL processes, and large datasets.
  • Solid foundation in software engineering principles for maintainable and scalable code.
  • Strong problem-solving ability and experience identifying actionable insights from complex data.
  • Creative thinker with the ability to build innovative solutions for live deals.

Preferred Qualifications, Capabilities, and Skills:

  • Familiarity with financial concepts, markets, and deal structures (e.g., ECM, DCM, M&A).
  • Prior experience working in or closely with investment banking teams is advantageous.

Why Join Us?

  • Be at the intersection of data and finance, driving innovation in a dynamic industry.
  • Work with a world-class team of bankers, data scientists, and engineers to develop impactful solutions.
  • Deliver real-world impact by contributing to high-profile deals and client engagements.

About Us

J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations, governments, 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 our company. 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 accommodations 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 accommodation.

About the Team

J.P. Morgan's Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.

#J-18808-Ljbffr

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.

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Education Jobs

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Jobs

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.

🍪 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.