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- Tasks: Design and implement ML solutions to enhance investment processes and client experiences.
- Company: Join J.P. Morgan, a global leader in financial services with a focus on diversity.
- Benefits: Competitive salary, inclusive culture, and opportunities for continuous learning.
- Why this job: Make a real impact in finance using cutting-edge AI and ML technologies.
- Qualifications: Master's or PhD in a STEM field, experience in AI/ML, and strong programming skills.
- Other info: Collaborative environment with excellent career growth and a commitment to diversity.
The predicted salary is between 43200 - 72000 £ per year.
As an innovative data scientist within the Data Science team, you will design and implement ML solutions to enhance investment processes, elevate client experiences, and streamline operations. Initially, you will be focused on developing solutions to support our ESG and Stewardship functions with a heavy focus on content extraction, search and principles‑based reasoning with LLMs. Your technical expertise will drive impactful results, and you'll play a key role in shaping our data science capabilities. You'll thrive in a collaborative culture that values hands‑on problem solving and continuous learning.
Job Responsibilities
- Collaborate with internal stakeholders to understand business needs, build out requirements, and design technical architectures.
- Develop technical solutions utilising LLMs with a focus on problems involving search, content extraction and principles‑based reasoning.
- Build comprehensive evaluation packages to ensure the efficacy and reliability of solutions and to build trust with stakeholders.
- Collaborate heavily with engineering functions to deliver high quality, scalable output.
- Stay up to date with the latest developments in AI and become an SME within the data science function.
Required qualifications, capabilities, and skills
- Master's degree or a PhD in Computer Science or Engineering or a quantitative or STEM discipline.
- Experience delivering AI/ML product and managing stakeholder relationships.
- Proven experience in NLP and working with LLMs.
- Proficiency in programming languages such as Python and familiarity with ML libraries and frameworks.
- Excellent communication skills and ability to work collaboratively in a fast‑paced, dynamic environment.
- Good understanding of the foundational principles and practical implementations of statistics and machine learning algorithms such as supervised and unsupervised learning, deep learning, reinforcement learning, etc.
Preferred qualifications, capabilities, and skills
- Strong analytical skills with an understanding of financial markets and asset management line of business.
- Business domain knowledge in ESG, investment stewardship, or buyside investment.
- CFA or equivalent financial qualification.
Senior Data Scientist employer: Jpmorgan Chase & Co.
Contact Detail:
Jpmorgan Chase & Co. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Scientist
✨Tip Number 1
Network like a pro! Reach out to your connections in the data science field, especially those who work at J.P. Morgan or similar companies. A friendly chat can lead to insider info about job openings and even referrals.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those involving LLMs and NLP. This will give you an edge during interviews and demonstrate your hands-on experience.
✨Tip Number 3
Prepare for technical interviews by brushing up on your programming skills, particularly in Python. Practice coding challenges and be ready to discuss your thought process when solving problems.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, it shows you’re genuinely interested in joining the team at J.P. Morgan.
We think you need these skills to ace Senior Data Scientist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Senior Data Scientist role. Highlight your experience with ML solutions, NLP, and LLMs, as these are key for us. Use specific examples that showcase your skills and achievements in these areas.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Share your passion for data science and how you can contribute to our ESG and Stewardship functions. Be sure to mention any relevant projects or experiences that align with our mission at StudySmarter.
Showcase Your Technical Skills: We want to see your technical prowess! Include details about your proficiency in Python and any ML libraries you've worked with. If you've developed solutions involving content extraction or principles-based reasoning, make sure to highlight those experiences.
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 shows you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at Jpmorgan Chase & Co.
✨Know Your ML Inside Out
Make sure you brush up on your machine learning knowledge, especially around LLMs and NLP. Be ready to discuss specific projects where you've implemented these technologies, as well as the challenges you faced and how you overcame them.
✨Showcase Your Collaboration Skills
Since this role involves working closely with internal stakeholders and engineering teams, prepare examples that highlight your collaborative experiences. Think of times when you successfully gathered requirements or built trust with stakeholders through effective communication.
✨Stay Current with AI Trends
Demonstrate your passion for continuous learning by discussing recent developments in AI and how they could impact the financial services industry. This shows you're not just knowledgeable but also proactive about staying ahead in your field.
✨Prepare for Technical Questions
Expect technical questions related to programming languages like Python and ML frameworks. Brush up on your statistics and machine learning algorithms, and be ready to explain how you've applied these concepts in real-world scenarios.
