Applied AI ML Lead

J.P. Morgan; Glasgow; On-site; GBP 60,000 - 100,000; Full time

Job summary

Join a forward-thinking company that is at the forefront of integrating machine learning into risk management. As an Applied AI/ML Engineer, you will tackle complex business challenges by leveraging extensive data resources and advanced algorithms. Your role will involve designing end-to-end AI solutions, collaborating with a talented team, and presenting insights to stakeholders. This position offers an exciting opportunity to work with cutting-edge technologies and make a significant impact in the financial services sector. If you're passionate about data science and looking to drive innovation, this role is perfect for you.

Qualifications

• Proven experience in quantitative fields such as Data Science or Computer Science.
• Expertise in statistical inference and data wrangling using Python.

Responsibilities

• Design and architect solutions in AI, focusing on anomaly detection and generative AI.
• Collaborate with data scientists to deploy machine learning solutions.

Skills

Python

Machine Learning

Data Wrangling

Statistical Inference

NLP

Problem Solving

Communication Skills

Education

Master's Degree in Data Science

PhD in a Quantitative Field

Tools

AWS

Spark

NumPy

pandas

scikit-learn

TensorFlow

PyTorch

SQL

GitHub

LangChain

Job description

The Risk Management & Corporate Technology Machine Learning team at JPMorgan Chase is dedicated to addressing complex business challenges through the application of data science and machine learning techniques across Risk, Compliance, Conduct, and Operational Risk. As an Applied AI/ML Engineer on the team, you will have the opportunity to explore intricate business problems and apply advanced algorithms to develop, test, and evaluate AI/ML applications or models for these challenges.

You will leverage the firm’s extensive data resources from both internal and external sources using Python, Spark, and AWS, among other systems. You are expected to extract business insights from technical results and effectively communicate them to a non-technical audience.

Job Responsibilities

• Design and architect end to end solutions in AI domain ranging from Anomaly detection Use cases, Chat with your at data, and using GenAI.
• Proactively develop an understanding of key business problems and processes.
• Execute tasks throughout the model development process, including data wrangling/analysis, model training, testing, and selection.
• Generate structured and meaningful insights from data analysis and modelling exercises, and present them in an appropriate format according to the audience.
• Collaborate with other data scientists and machine learning engineers to deploy machine learning solutions.
• Conduct ad-hoc and periodic analysis as required by business stakeholders, the model risk function, and other groups.

Required qualifications, capabilities, and skills

• Proven experience post-advanced degree (MS, PhD) in a quantitative field (e.g., Data Science, Computer Science, Applied Mathematics, Statistics, Econometrics).
• Experience in statistical inference and experimental design (such as probability, linear algebra, calculus).
• Data wrangling: understanding complex datasets, cleaning, reshaping, and joining messy datasets using Python.
• Practical expertise and work experience with ML projects, both supervised and unsupervised.
• Proficient programming skills with Python, including libraries such as NumPy, pandas, and scikit-learn, as well as R.
• Understanding and usage of the OpenAI API.
• NLP: tokenization, embeddings, sentiment analysis, basic transformers for text-heavy datasets.
• Experience with LLM & Prompt Engineering, including tools like LangChain, LangGraph, and Retrieval-Augmented Generation (RAG).
• Experience in anomaly detection techniques, algorithms, and applications.
• Excellent problem-solving, communication (verbal and written), and teamwork skills.

Preferred qualifications, capabilities, and skills

• Experience with deep learning frameworks such as TensorFlow and PyTorch.
• Experience with big data frameworks, with a preference for Databricks.
• Experience with databases, including SQL (Oracle, Aurora), and Vector DB.
• Familiarity with version control systems such as Bitbucket and GitHub.
• Experience with graph analytics and neural networks.
• Experience working with engineering teams to operationalize machine learning models.
• Familiarity with the financial services industry.