Quantitative Research - CDO Data Solution Architect - Vice President

J.P. Morgan

London

10 months ago

Applications closed

Related Jobs

View all jobs

Senior Quantitative Researcher — Deep Learning for Markets

Machine Learning Engineer

Machine Learning Quantitative Researcher

Quantitative Data Engineer: Build Trading Data Pipelines

Senior Data Scientist (Commodities)

Machine Learning Workflow Engineer

Are you passionate about data architecture and innovation? Join us as a Data Solutions Architect to bridge our Chief Data Office and Technology teams, designing advanced tooling to support our Markets Data Strategy. Lead the development of proof-of-concept prototypes and shape the future of our data initiatives.

Job summary:

As a Data Solutions Architect within Quantitative Research, you will play a crucial role in designing and promoting the adoption of advanced data tooling. You will prototype technical patterns for implementing Markets Data Standards and provide expert guidance on data usage within Markets. Your work will prioritize the delivery of technology data tooling, ensuring seamless integration with data and analytics platforms. Join us to drive data strategy through innovative solutions.

In this role, you will focus on automating data lineage registration and data quality monitoring. You will work with various technical teams to integrate strategic data management tools into workflows, enhancing our data management capabilities. If you have a strong background in data architecture and a passion for innovation, we invite you to join our team and make a significant impact on our data strategy.

Job Responsibilities:

Act as a bridge between the Chief Data Office and Technology teams
Design and promote the adoption of tooling to support the Markets Data Strategy
Develop proof-of-concept prototypes for cross-LOB priority data products
Prototype technical patterns for Markets Data Standards implementation
Automate data lineage registration and data quality monitoring
Provide guidance on data patterns usage within Markets
Prioritize technology data tooling deliveries supporting Markets
Ensure integration with data and analytics platforms and on-premises data stores
Integrate strategic data management tools into producer and consumer workflows

Required Qualifications, Capabilities, and Skills:

You have bachelor’s or master’s degree in Computer Science, Engineering, or related field
You have 5-10 years of experience in Financial Services technology
You have experience with data/technology projects in the Financial Services sector
You demonstrate excellent Python programming skills
You have proven experience as a Data Engineer or similar role
You have ability to build and optimize data sets and 'big data' pipelines
You are familiar with cloud services like AWS, Azure, or GCP
You demonstrate ability to work collaboratively across multiple technology teams

Preferred Qualifications, Capabilities, and Skills:

You have prior experience with Sell-Side analytics platforms (Athena, SecDB, etc.)

#J-18808-Ljbffr

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

Industry Insights

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

Jan 18, 2026

Careers Jobs

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

Jan 13, 2026

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.

Jan 6, 2026

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.