Applied AI ML Lead - Senior Machine Learning Scientist – Machine Learning for Technology in London

Applied AI ML Lead - Senior Machine Learning Scientist – Machine Learning for Technology in London

London Full-Time 80000 - 100000 £ / year (est.) No working from home possible
JPMorgan Chase

At a Glance

  • Tasks: Lead innovative machine learning projects to solve real-world problems in finance and technology.
  • Company: Join J.P. Morgan, a global leader in financial services with a focus on innovation.
  • Benefits: Competitive salary, diverse work environment, and opportunities for professional growth.
  • Other info: Collaborative team culture with a commitment to diversity and inclusion.
  • Why this job: Make a significant impact by transforming how the bank operates through advanced AI solutions.
  • Qualifications: PhD or MS in a quantitative field with hands-on machine learning experience.

The predicted salary is between 80000 - 100000 £ per year.

Join the Applied Innovation of AI team, a premier machine learning group within the Chief Technology Office of JP Morgan Chase. We tackle crucial business priorities using innovative machine learning techniques, focusing on Software, Cybersecurity, and Technology Infrastructure. As an Applied AI ML Lead - Senior Machine Learning Scientist within the Applied Innovation of AI (AI2) team, you will apply sophisticated machine learning methods to a wide variety of complex tasks, collaborate closely with stakeholders, and invest independent time towards learning, researching, and experimenting with new innovations in the field. This role offers a unique opportunity to explore novel and complex challenges that could profoundly transform how the bank operates.

Job responsibilities

  • Research and explore new machine learning methods through independent study, attending industry-leading conferences and experimentation.
  • Develop state-of-the art machine learning models to solve real-world problems and apply it to complex business critical problems in Cybersecurity, Software and Technology Infrastructure.
  • Collaborate with multiple partner teams in Cybersecurity, Software and Technology Infrastructure to deploy solutions into production.
  • Drive firmwide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business.
  • Contribute to reusable code and components that are shared internally.

Required qualifications, capabilities and skills

  • PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science. Or an MS with full time industry or research experience in the field.
  • Hands-on experience and solid understanding of machine learning and deep learning methods.
  • Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas).
  • Scientific thinking and the ability to invent.
  • Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals.
  • Experience with big data and scalable model training.
  • Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences.
  • Curious, hardworking and detail-oriented, and motivated by complex analytical problems.
  • Ability to work both independently and in highly collaborative team environments.

Preferred qualifications, capabilities and skills

  • Strong background in Mathematics and Statistics.
  • Familiarity with the financial services industries.
  • Experience with A/B experimentation and data/metric-driven product development.
  • Experience with cloud-native deployment in a large scale distributed environment.
  • Knowledge of large language models (LLMs) and accompanying toolsets in the LLM ecosystem (e.g. Langchain, Vector databases, opensource Hugging Face Models).
  • Knowledge in Reinforcement Learning or Meta Learning.
  • Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal.
  • Ability to develop and debug production-quality code.
  • Familiarity with continuous integration models and unit test development.

ABOUT US

J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world’s most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives. We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs.

ABOUT THE TEAM

Our Corporate Technology team relies on smart, driven people like you to develop applications and provide tech support for all our corporate functions across our network. Your efforts will touch lives all over the financial spectrum and across all our divisions: Global Finance, Corporate Treasury, Risk Management, Human Resources, Compliance, Legal, and within the Corporate Administrative Office. You’ll be part of a team specifically built to meet and exceed our evolving technology needs, as well as our technology controls agenda.

Applied AI ML Lead - Senior Machine Learning Scientist – Machine Learning for Technology in London employer: JPMorgan Chase

At J.P. Morgan, we pride ourselves on being a premier employer that fosters innovation and collaboration within our Applied Innovation of AI team. Located in a dynamic financial hub, we offer our employees unparalleled opportunities for growth through cutting-edge projects in machine learning and cybersecurity, alongside a commitment to diversity and inclusion. Join us to be part of a culture that values your contributions and encourages continuous learning in a supportive environment.

JPMorgan Chase

Contact Details:

JPMorgan Chase Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Applied AI ML Lead - Senior Machine Learning Scientist – Machine Learning for Technology in London

Tip Number 1

Network like a pro! Reach out to folks in the industry, especially those at JP Morgan. Attend meetups or conferences related to AI and machine learning. You never know who might have the inside scoop on job openings or can put in a good word for you.

Tip Number 2

Show off your skills! Create a portfolio showcasing your machine learning projects. Whether it's a GitHub repo or a personal website, make sure it highlights your best work. This is your chance to demonstrate your expertise beyond just a CV.

Tip Number 3

Prepare for interviews by brushing up on your technical knowledge. Be ready to discuss your experience with tools like TensorFlow and PyTorch. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with both techies and non-techies.

Tip Number 4

Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, keep an eye on our careers page for new opportunities that match your skills and interests. We’re always looking for talented individuals like you!

We think you need these skills to ace Applied AI ML Lead - Senior Machine Learning Scientist – Machine Learning for Technology in London

Machine Learning
Deep Learning
TensorFlow
PyTorch
NumPy
Scikit-Learn
Pandas

Some tips for your application 🫡

Show Off Your Skills:When you’re writing your application, make sure to highlight your hands-on experience with machine learning and deep learning methods. We want to see how you've tackled real-world problems using tools like TensorFlow or PyTorch, so don’t hold back!

Tailor Your Application:Make your application stand out by tailoring it to the role. Use keywords from the job description, especially around collaboration and innovation in AI. This shows us that you understand what we’re looking for and how you fit into our team.

Be Clear and Concise:We appreciate clarity! When explaining your past projects or experiences, keep it straightforward. Use bullet points if needed, and ensure your technical concepts are easy to understand for both technical and non-technical audiences.

Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about our team and culture.

How to prepare for a job interview at JPMorgan Chase

Know Your Machine Learning Stuff

Make sure you brush up on the latest machine learning techniques and tools like TensorFlow and PyTorch. Be ready to discuss your hands-on experience and how you've applied these methods to solve real-world problems, especially in areas like Cybersecurity and Technology Infrastructure.

Show Off Your Research Skills

Since this role involves a lot of independent study and experimentation, be prepared to talk about any research you've conducted or conferences you've attended. Highlight your curiosity and how you stay updated with the latest innovations in machine learning.

Collaboration is Key

This position requires working closely with various teams, so demonstrate your ability to collaborate effectively. Share examples of past projects where you worked with cross-functional teams and how you communicated complex technical concepts to non-technical stakeholders.

Prepare for Technical Questions

Expect to face some challenging technical questions during the interview. Brush up on your knowledge of big data, scalable model training, and A/B testing. Practise explaining your thought process when designing experiments and evaluating model performance metrics.