Data Science & Machine Learning - Senior Associate - Asset Management

JPMorgan Chase & Co.
London
5 days ago
Create job alert

The . Morgan Asset Management Data Science Team (JPMAM) is part of . Morgan Asset & Wealth
Management. The JPMAM Data Science team focuses primarily on developing novel AI/ML methods to drive innovative solutions for amplifying data-driven investment decision-making, improved client engagement, and operational effectiveness.

Job Summary­­

As a Data Scientist Senior Associate in the . Morgan Asset Management Data Science Team (JPMAM), you will work closely with investment and data science professionals across Asset Management to proactively source, due diligence and draw insights from in-house investment and alternative data.

This is an exciting opportunity to join a small, dynamic team with the resources and impact of one of the world’s largest companies. We are looking for problem-solvers with a passion for building greenfield analytic solutions and helping to scale them for greater impact. The successful candidate will be expected to work on projects along the full data science spectrum. From data acquisition and wrangling, to model selection to presentation and data visualization. The role requires the successful candidate to work as a part of a globally distributed data science team. They will work with stakeholders and subject matter experts to understand problems then find innovative, practical solutions. The successful candidate will be able to evidence a history of delivery and innovation.

Job Responsibilities

Building tools and systems to understand decision data, context and events around it to enhance JPMAM’s decision attribution capabilities and systematically identify opportunities Working with portfolio managers to understand sources of alpha and opportunities to improve decision-making process  Working with partners in technology and user experience to build out tools providing real-time insights to portfolio managers and their teams Given the subject matter, a non-financial background would be acceptable if the candidate had an exceptionally strong data science skillset 

Required qualifications, capabilities and skills 

Masters Degree or PhD, in computer science, statistics, or other quantitative field Strong analytical/modelling skills and business orientation with proven ability to use data and analytics to drive business results; strong technical background Demonstrated experience working within a data science teamTimeseries analysis and modellingTraining and fine-tuning of the ML model for investment modelsStrong knowledge of Python for data scientists (., pandas), traditional ML and deep learning libraries (., scikit learn, xgboost, TensorFlow, Torch, Data manipulation languages (., SQL)Data visualization / presentation skills (., Tableau)  Demonstrated experience working with engineering, developers and other technology teamsWriting production quality code, unit testing and familiarity with version control Familiarity with cloud-based technologies Demonstrated experience using alternative datasets in investing and alpha researchIn-depth understanding of financial markets required Strong communications skills and the ability to present findings to a non-technical audience Passion for learning and adopting a wide range of techniques in an agile environment

Preferred qualifications, capabilities, and skills

Prior experience working in alpha capture, performance attribution or trading / decision analytics role Front office experience in finance (preferably buyside) Familiarity in incorporating unstructured data into investment research Knowledge of alternative data landscape CFA

Related Jobs

View all jobs

Data Science & Machine Learning Ops. Enablement Specialist

Senior Lead Analyst - Data Science - Machine Learning & Gen AI - UK

Western Europe Practice Head - Data Science (Machine Learning/Artificial Intelligence (ML/AI)

Data Science Specialist

Faculty Fellowship Programme - Data Science - May 2026

Data Scientist

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 AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

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

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.