Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Science Senior Associate - LLM/NLQ - Asset Management Data Analytics

J.P. MORGAN-1
City of London
3 days ago
Create job alert
Overview

Are you passionate about data science and eager to make a real impact in asset management? As an NLQ/LLM Data Scientist, you'll help transform investment processes and client experiences with innovative natural language and machine learning solutions. You'll collaborate with talented teams, continuously learn, and drive meaningful change using the latest data science techniques. Join us to advance your career and shape the future of investment management.

As an NLQ/LLM Data Scientist in the Asset Management Data & Analytics team, you will design and implement natural language interfaces that enhance decision-making and optimize operational processes. You will work closely with business stakeholders, technologists, and control partners to deploy solutions into production. Your expertise will generate actionable insights and improve client experiences, while you stay at the forefront of data science innovation.

Job Responsibilities
  • Collaborate with internal stakeholders to identify business needs and develop NLQ solutions that drive transformation
  • Apply large language models, machine learning techniques, and statistical analysis to enhance decision-making and workflow efficiency
  • Collect and curate datasets for evaluation and continuous improvement
  • Perform data science experiments, such as building model architectures, hyperparameter tuning, and evaluations
  • Monitor and improve model performance through feedback and active learning
  • Work with technology teams to deploy and scale models in production
  • Deliver written, visual, and oral presentations of modeling results to stakeholders
  • Stay current with research in LLM, ML, and data science, leveraging emerging techniques for ongoing enhancement
Required Qualifications, Capabilities, and Skills
  • Degree in a quantitative or technical discipline, or practical industry experience
  • Proficient Python programming skills with production-quality coding experience
  • Experience working with structured and unstructured data
  • Experience in prompt engineering and domain adaptation
  • Understanding of foundational ML algorithms such as clustering and decision trees
  • Ability to communicate complex concepts and results to technical and business audiences
  • Active interest in applying ML solutions to investment management
Preferred Qualifications, Capabilities, and Skills
  • Experience in data science roles such as data engineering, ML engineering, LLM engineering, or data analytics
  • Proficiency with SQL and Snowflake
  • Experience in Asset Management
  • Experience applying NLP, LLM, and ML techniques to solve business problems such as semantic search, information extraction, question answering, summarization, personalization, classification, or forecasting

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

Related Jobs

View all jobs

Data Science Senior Associate - LLM/NLQ - Asset Management Data Analytics

Data Science Senior Associate - LLM/NLQ - Asset Management Data Analytics

Data Science Senior Associate - LLM/NLQ - Asset Management Data Analytics

Data Scientist Senior Associate - AI

Quantitative Valuations Senior Associate

Associate/ Senior Associate, Quantitative Modeling and Strategies

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.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.