Risk Management - Data Scientist Associate

JPMorgan Chase & Co.
Bournemouth
3 days ago
Create job alert
Job summary

Bring your expertise to JPMorgan Chase. As part of Risk Management and Compliance, you are at the center of keeping JPMorgan Chase strong and resilient. You help the firm grow its business in a responsible way by anticipating new and emerging risks, and using your expert judgement to solve real-world challenges that impact our company, customers and communities. Our culture in Risk Management and Compliance is all about challenging the status quo and striving to be best in class.

As a Data Scientist Associate in our Data Science team, you will explore, pilot, and implement transformative AI solutions—including GenAI—in collaboration with Product, Engineering, and Lines of Business. In this pivotal role, you’ll join a team that values experimentation, delivery, and continuous learning, where your ideas are encouraged and operational excellence is a shared goal. You will leverage your expertise in data and advanced analytics to develop robust tools and solutions using the latest AI/ML techniques, cloud technologies, data mesh architectures, and enterprise knowledge bases. Together, we design scalable, high-quality internal solutions that integrate seamlessly into business operations, driving adoption and effective model management. By collaborating with business units, technology partners, and stakeholders, we enable our AI/ML solutions to deliver real business value and become embedded into our workflows for lasting impact.

Job responsibilities
  • Apply AI/ML techniques—including LLMs, Generative AI, NLP, and coding assistants—to solve business problems and enhance product capabilities.
  • Collaborate with product, business, and cross-functional teams to define project goals, requirements, and plans, participating in stakeholder-facing communications as needed and assist in designing experiments, implementing algorithms, validating results, and productionizing trustworthy, explainable AI/ML solutions.
  • Deliver projects using Agile/sprint methodologies, translating business requirements into technical specifications, and supporting milestone achievement.
  • Write secure, high-quality production code, review and debug code, and contribute to documentation of code repositories and technical challenges.
  • Integrate advanced analytics models and applications into operational workflows to facilitate business value and adoption.
  • Implement robust drift monitoring and model retraining processes to maintain accuracy and performance (ongoing performance monitoring).
Required qualifications, capabilities and skills
  • Bachelor’s or Master’s degree in engineering, computer science, statistics, mathematics or a related technical or quantitative field
  • Proven track record of deploying, AI, ML, and advanced analytics models in a large-scale enterprise environment.
  • Hands-on experience with AI/ML algorithms, statistical modeling, and scalable data processing pipelines.
  • Proficiency with machine learning frameworks such as Tensorflow, Pytorch, Scikit-Learn, and agentic workflows and frameworks such as LangChain, LangGraph, and Auto-GPT.
  • Ability to convey technical concepts and results to both technical and business audiences.
  • Scientific mindset with a proven ability to innovate and work both independently and collaboratively within a team.
  • Ability to thrive in a matrixed environment and build effective partnerships with colleagues at various levels and across multiple locations.


#J-18808-Ljbffr

Related Jobs

View all jobs

Risk Management Internship | Data Analytics & Claims

Risk Management - Data Scientist Associate

Quantitative Engineer - Risk Engine & Platform Innovation

Quantitative Developer

Quantitative Risk & Treasury Graduate Programme

Risk Systems & Data Analytics Manager

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

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.

Education

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.

Careers Jobs

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

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

🍪 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.