At a Glance
- Tasks: Explore and develop cutting-edge machine learning models to tackle real-world challenges.
- Company: Join JP Morgan Chase's elite AI team, driving innovation in technology.
- Benefits: Competitive salary, health benefits, and opportunities for professional growth.
- Other info: Collaborative environment with access to industry-leading resources and career advancement.
- Why this job: Make a significant impact in the financial sector using advanced AI technologies.
- Qualifications: Masters or PhD in relevant fields with hands-on machine learning experience.
The predicted salary is between 36000 - 60000 £ per year.
The Applied Innovation of AI (AI2) team is an elite machine learning group located within the Tech CDO at JP Morgan Chase. Strategically positioned in the Chief Technology Office, our work spans across Cybersecurity, Global Technology Infrastructure and the Software Development Lifecycle (SDLC). With this unparalleled access to technology groups in the firm, the role offers a unique opportunity to explore novel and complex challenges that could profoundly transform how the bank operates.
As a Machine Learning Scientist, you will apply sophisticated machine learning methods to a wide variety of complex tasks including:
- Data mining and exploratory data analysis and visualization
- Text understanding and embedding
- Anomaly detection in time series and log data
- Large language models (LLMs) and generative AI for technology use-cases
- Reinforcement learning and recommendation systems
You must excel in working in a highly collaborative environment together with the business, technologists and control partners to deploy solutions into production. You must also have a passion for machine learning and invest independent time towards learning, researching and experimenting with new innovations in the field. You must have solid expertise in Deep Learning with hands-on implementation experience and possess strong analytical thinking, a deep desire to learn and be highly motivated.
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 and also externally.
Required qualifications, capabilities and skills
- Masters in a related discipline (e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science) with 2 years experience or PhD with 1 year of 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).
- Extensive experience with large language models (LLMs) and accompanying tools & techniques in the LLM ecosystem (e.g. LangChain, LangGraph, Vector databases, opensource Models, RAG, Agentic Systems & Workflows, LLM fine-tuning).
- 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.
- In-depth knowledge and proficiency in agentic frameworks like LangChain and LangGraph and related platforms like LangSmith.
- Familiarity with the financial services and related technologies and industries, including familiarity in networking and infrastructure platforms.
- Experience with A/B experimentation and data/metric-driven product development.
- Experience with cloud-native deployment in a large scale distributed environment.
- 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.
Applied AI and Machine Learning Scientist in London employer: JPMorganChase
At JP Morgan Chase, we pride ourselves on being an exceptional employer, particularly within our elite Applied Innovation of AI (AI2) team. Located in a dynamic and collaborative environment, we offer unparalleled access to cutting-edge technology and the opportunity to tackle complex challenges that can significantly impact the banking industry. Our commitment to employee growth is evident through continuous learning opportunities, participation in industry-leading conferences, and a culture that fosters innovation and teamwork, making it an ideal place for passionate machine learning professionals to thrive.
StudySmarter Expert Advice🤫
We think this is how you could land Applied AI and Machine Learning Scientist in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at JP Morgan Chase or similar firms. Attend meetups, webinars, and conferences to make connections that could lead to job opportunities.
✨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, having tangible examples of your work can really impress potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice coding challenges and be ready to discuss your past projects in detail, especially how they relate to AI and machine learning.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of resources to help you land that dream job, so take advantage of everything we offer to boost your chances.
We think you need these skills to ace Applied AI and Machine Learning Scientist in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the role of Applied AI and Machine Learning Scientist. Highlight your experience with machine learning methods, deep learning toolkits, and any relevant projects that showcase your skills in data mining and exploratory analysis.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to express your passion for machine learning and how your background aligns with the unique challenges at JP Morgan Chase. Don’t forget to mention your collaborative spirit and eagerness to learn!
Showcase Your Projects:If you’ve worked on any interesting machine learning projects, make sure to include them in your application. Whether it's a personal project or something from your previous job, demonstrating your hands-on experience can really set you apart.
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It’s the best way to ensure your application gets the attention it deserves, and we can’t wait to see what you bring to the table!
How to prepare for a job interview at JPMorganChase
✨Know Your Machine Learning Stuff
Make sure you brush up on your machine learning and deep learning methods. Be ready to discuss specific projects where you've applied these techniques, especially with tools like TensorFlow or PyTorch. They’ll want to see your hands-on experience, so be prepared to dive into the nitty-gritty details.
✨Show Off Your Collaboration Skills
Since this role involves working closely with various teams, highlight your collaborative experiences. Share examples of how you've successfully partnered with others to deploy solutions or tackle complex problems. This will show that you can thrive in a team-oriented environment.
✨Demonstrate Your Curiosity
Express your passion for continuous learning and innovation in AI. Talk about any recent research, conferences you've attended, or new techniques you've experimented with. This will demonstrate your commitment to staying at the forefront of the field.
✨Prepare for Technical Questions
Expect some technical questions that test your understanding of machine learning concepts and frameworks. Practice explaining your thought process when designing experiments or evaluating model performance. Being able to communicate complex ideas clearly is key!
