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- Tasks: Join the AI2 team to tackle business challenges using cutting-edge machine learning techniques.
- Company: J.P. Morgan is a global leader in financial services, known for innovation and client partnerships.
- Benefits: Enjoy a collaborative environment, opportunities for growth, and a commitment to diversity and inclusion.
- Why this job: Be at the forefront of technology, solving real-world problems while working with top professionals.
- Qualifications: PhD or MS in a quantitative field with hands-on machine learning experience required.
- Other info: Flexible work arrangements and a focus on continuous learning and development.
The predicted salary is between 43200 - 72000 £ per year.
Join the elite Applied Innovation of AI (AI2) team at JP Morgan Chase, strategically located within the CTO office. As a Machine Learning Specialist within the JPMC businesses, you will be responsible for addressing business-critical priorities using innovative machine learning techniques. You will work closely with stakeholders to execute projects that support the growth of the business and explore novel challenges that could revolutionise the way the bank operates. Your role will involve applying advanced machine learning methods to a range of complex tasks, such as data mining, text understanding, anomaly detection, and generative AI. You will collaborate with business, technologists, and control partners to deploy solutions into production. Additionally, your responsibilities will include researching new methods, developing models, and contributing to reusable code and components.
Job Responsibilities:
- Research and explore new machine learning methods through independent study, attending conferences, and experimentation.
- Develop state-of-the-art machine learning models to solve real-world problems in Cybersecurity, Software, and Technology Infrastructure.
- Collaborate with partner teams to deploy solutions into production.
- Drive firmwide initiatives by developing large-scale frameworks to accelerate the application of machine learning models.
- Contribute to reusable code and components shared internally and externally.
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 industry or research experience.
- 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 evaluate 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.
- Curious, hardworking, detail-oriented, and motivated by complex analytical problems.
- Ability to work both independently and in collaborative team environments.
Preferred Qualifications, Capabilities, and Skills:
- 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 (e.g., Langchain, Vector databases, open-source 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.
Contact Detail:
JPMorgan Chase & Co. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Applied AI ML Associate - Machine Learning Scientist - Machine Learning for Technology
✨Tip Number 1
Familiarise yourself with the latest advancements in machine learning and deep learning. Attend relevant conferences or webinars to network with industry professionals and gain insights into cutting-edge techniques that could set you apart.
✨Tip Number 2
Showcase your hands-on experience with machine learning toolkits like TensorFlow and PyTorch. Consider contributing to open-source projects or creating your own projects to demonstrate your skills and understanding of these technologies.
✨Tip Number 3
Develop a strong understanding of A/B testing and data-driven product development. Being able to discuss how you've applied these concepts in previous projects can highlight your practical experience and analytical mindset.
✨Tip Number 4
Prepare to discuss your research and any published work in machine learning at major conferences. This not only showcases your expertise but also demonstrates your commitment to advancing the field, which is highly valued by employers like us.
We think you need these skills to ace Applied AI ML Associate - Machine Learning Scientist - Machine Learning for Technology
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning and deep learning. Include specific projects or research that demonstrate your hands-on skills with tools like TensorFlow, PyTorch, and Scikit-Learn.
Craft a Compelling Cover Letter: In your cover letter, express your passion for machine learning and how it aligns with the goals of JP Morgan Chase. Mention any relevant experience with A/B testing, cloud-native deployment, or published research to stand out.
Showcase Your Research Skills: If you have conducted independent studies or attended conferences related to machine learning, be sure to mention these experiences. Highlight any innovative methods you've explored or developed.
Demonstrate Communication Skills: Since effective communication is key for this role, include examples in your application that showcase your ability to explain complex technical concepts clearly, both in writing and verbally.
How to prepare for a job interview at JPMorgan Chase & Co.
✨Showcase Your Technical Skills
Be prepared to discuss your hands-on experience with machine learning and deep learning methods. Highlight specific projects where you've used toolkits like TensorFlow or PyTorch, and be ready to explain the models you developed and their impact.
✨Demonstrate Problem-Solving Abilities
Expect questions that assess your scientific thinking and ability to tackle complex analytical problems. Prepare examples of how you've designed experiments or evaluated model performance metrics in alignment with business goals.
✨Communicate Effectively
Since you'll need to collaborate with various stakeholders, practice explaining technical concepts in a clear and concise manner. Be ready to discuss how you've communicated results to non-technical audiences in previous roles.
✨Stay Curious and Informed
Show your passion for continuous learning by discussing recent advancements in machine learning. Mention any conferences you've attended or research you've conducted, especially in areas like reinforcement learning or large language models.
