Applied AI ML Lead

TN United Kingdom; Glasgow; On-site; GBP 60,000 - 100,000; Full time

Job summary

An innovative financial services firm is seeking an Applied AI/ML Engineer to tackle complex business challenges using data science and machine learning. In this role, you will design and implement advanced algorithms to develop AI/ML applications, leveraging extensive data resources. You will work with cutting-edge technologies like Python, Spark, and AWS to extract insights and communicate findings effectively to stakeholders. This position offers a unique opportunity to contribute to transformative projects while collaborating with a talented team of data scientists and engineers. If you're passionate about AI and eager to make an impact, this is the perfect role for you.

Qualifications

• Advanced degree in Data Science or related field with proven experience.
• Strong understanding of statistical methods and data analysis.

Responsibilities

• Design end-to-end AI solutions including anomaly detection and chatbots.
• Collaborate with teams to deploy machine learning solutions.

Skills

Statistical Inference

Data Wrangling

Python Programming

Machine Learning

Natural Language Processing

Problem-Solving

Communication Skills

Education

Master's Degree in Data Science

PhD in a Quantitative Field

Tools

Python Libraries (NumPy, pandas, scikit-learn)

R Programming

TensorFlow

PyTorch

Databricks

OpenAI API

Bitbucket

GitHub

Job description

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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

1. Design and architect end-to-end solutions in the AI domain, including anomaly detection, chatbots, and GenAI applications.
2. Proactively develop an understanding of key business problems and processes.
3. Execute tasks throughout the model development process, including data wrangling/analysis, model training, testing, and selection.
4. Generate structured and meaningful insights from data analysis and modeling exercises, and present them appropriately to the audience.
5. Collaborate with other data scientists and machine learning engineers to deploy machine learning solutions.
6. 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 such as Data Science, Computer Science, Applied Mathematics, Statistics, or Econometrics.
• Experience in statistical inference and experimental design, including probability, linear algebra, and calculus.
• Data wrangling skills: understanding complex datasets, cleaning, reshaping, and joining messy datasets using Python.
• Practical expertise with ML projects, both supervised and unsupervised.
• Proficient programming skills with Python (libraries like NumPy, pandas, scikit-learn) and R.
• Understanding and experience with the OpenAI API.
• Experience with NLP techniques such as tokenization, embeddings, sentiment analysis, and transformers for text-heavy datasets.
• Knowledge of LLM & Prompt Engineering, including tools like LangChain, LangGraph, and Retrieval-Augmented Generation (RAG).
• Experience with anomaly detection techniques and algorithms.
• Excellent problem-solving, communication, and teamwork skills.

Preferred qualifications, capabilities, and skills

• Experience with deep learning frameworks like TensorFlow and PyTorch.
• Experience with big data frameworks, especially Databricks.
• Knowledge of 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 collaborating with engineering teams to operationalize machine learning models.
• Familiarity with the financial services industry.