Quantitative Research - Athena Analytics Developer - Executive Director

JPMorgan Chase & Co.
Greater London
10 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Capacity Planning Data Scientist

Data Science Consultant

Quantitative Analyst

Snr Technical Apps Specialist - Quantitative Risk Management

Snr Technical Apps Specialist - Quantitative Risk Management

Quantitative Researchers (QR) are key part of JP Morgan’s markets business, developing and maintaining sophisticated mathematical models, cutting-edge methodologies and infrastructure to value and risk manage financial transactions. We develop these in Athena, which is a next generation risk, pricing, and trade management platform built in-house at JP Morgan.

Job summary: 


As a Executive Director within Quantitative Research Athena and Analytics team, you will be focusing on cross asset topics ranging from pricing library and market model design, risk frameworks, UI design to high performance computing.


Athena is designed to enable rapid innovation on the desk by offering Quantitative Analysts, Risk Managers and Technologists a consistent, cross-asset portfolio of models, frameworks and tools to use in building financial applications. The power of the Athena platform derives from several key technical innovations: a powerful Dependency Graph implementation, a ubiquitous data store called Hydra, a Real-Time Risk Reporting framework, a robust Deal Model, and a forward propagating, event-driven graph called Reactive.


Job responsibilities:

Developing Athena (Python) analytics software that is used to price and risk manage financial products Designing efficient, scalable and usable cross asset frameworks with the aim of establishing golden standards across all QR streams Optimizing code and business processes, providing expert guidance to desk-aligned quant teams in using frameworks Support of end users of the frameworks, communicating with desk-aligned quant teams and technology groups.

Required qualifications, capabilities, and skills:

You have a degree in a quantitative field, . computer science, mathematics, engineering, physics You demonstrate outstanding problem solving skills You have excellent software and algorithm design and development skills You are passionate about software design and writing high quality code You demonstrate experience working in pricing libraries and risk management systems You have a good understanding of trade life cycle, MTM, PnL and other processes that govern day to day business operations You have excellent oral and written communication skills

Preferred qualifications, capabilities, and skills:

You have a knowledge of finance or quantitative finance You have experience writing high quality Python

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.

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

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.