Asset & Wealth Management - Equities Quantitative Investment Analyst - Associate/ Vice President

JPMorganChase
City of London
3 months ago
Applications closed

Related Jobs

View all jobs

Alpha Data Services, Performance Ready Data Analyst, EMEA Lead, Vice President

Data Analyst – Asset Optimisation

Data Analyst

Data Analyst – Asset Optimisation

Systematic Credit Quantitative Researcher

Systematic Credit Quantitative Researcher

Wealth Management - CIO Equities, Associate

J.P. Morgan Chase & Co. is a leading global financial services firm with assets of more than $2 trillion, over 265,000 employees and operations in over 60 countries. It operates across six business segments including Investment Banking, Commercial Banking, Treasury & Securities Services, Asset & Wealth Management, Retail Financial Services and Card Services.

The Global Wealth Management business offers individuals and families personalized, comprehensive financial solutions that integrate sophisticated investment management, capital markets, trust and banking capabilities. JPMorgan Private Bank was recognized in 2015 by Euromoney as the world\'s best global private bank, with more than 1,800 client advisors in 120 offices in 11 countries.

Role Summary

As an Equity Quantitative Investment Analyst / VP, you will join a growing and innovative Equity Portfolio Management team within Wealth Management\'s Chief Investment Officer Team (Private Bank CIO Team), reporting to the Head of Quantitative Investments in the CIO Equities team. In this role, you will be part of a group that combines traditional fundamental equity analysis with rigorous quantitative and factor-based approaches to drive investment decisions. You will collaborate closely with fundamental analysts and portfolio managers, monitor the trade process, and contribute quantitative investment and portfolio construction ideas for implementation within discretionary equity and multi-asset client portfolios across global markets.

Responsibilities
  • Risk Modeling: Support the application of risk models (Axioma or other risk models) to evaluate portfolio exposures and assist in risk management and investment decision-making across global equity markets.
  • Portfolio Construction: Assist portfolio managers in applying quantitative models and analytics to improve portfolio construction, evaluate risk exposures, and conduct performance attribution for global equity portfolios.
  • Quantitative Research: Help build and maintain financial models using programming skills (Python, R, or similar), and work with large and complex datasets to uncover new market insights and trends. Contribute to the application of AI and machine learning techniques to enhance investment research and portfolio management.
  • Collaboration and Communication: Collaborate with fundamental team members and senior quants to integrate quantitative insights into investment strategies. Clearly and effectively communicate quantitative concepts, findings, and actionable insights to portfolio managers and other investment professionals.
  • Compliance: Maintain a consistent focus on compliance and risk management.
Key Skills and Experience Required
  • 3-5 years\' experience in quantitative analysis, preferably on the buy-side or in investment management. Understanding of equity markets, financial theory, and risk models.
  • Experience applying or supporting risk models and portfolio construction; familiarity with Axioma or other risk models is a plus.
  • Proficient programming skills in Python, including experience with data analysis libraries (e.g., Pandas, NumPy) and working with APIs.
  • Familiarity with statistical analysis, econometrics, machine learning, and/or AI techniques.
  • Bachelor\'s or Master\'s degree in a quantitative field (Finance, Mathematics, Engineering, Computer Science, etc.).
  • Progress toward CFA designation is a plus.
Key Attributes
  • Strong analytical mindset with intellectual curiosity, problem-solving, and critical thinking skills, as well as excellent attention to detail.
  • Excellent communication skills (listening, verbal, and written), with the ability to explain quantitative concepts to non-quant colleagues.
  • Clear passion for financial markets and investing.
  • High-level interpersonal and teamwork skills.
  • Effective multi-tasking and prioritization capabilities.
  • Ability to operate productively in a collaborative, fast-paced, team-oriented environment.
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 Asset & Wealth Management delivers industry-leading investment management and private banking solutions. Asset Management provides individuals, advisors and institutions with strategies and expertise that span the full spectrum of asset classes through our global network of investment professionals. Wealth Management helps individuals, families and foundations take a more intentional approach to their wealth or finances to better define, focus and realize their goals.


#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.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.