As an Applied AI ML Lead - Data Scientist- Vice President within the AI/ML team at JPMorgan Chase, you’ll leverage your technical expertise and leadership abilities to support AI innovation. You should have deep knowledge of AI/ML and effective leadership to inspire the team, align cross-functional stakeholders, engage senior leadership, and promote business results.
Job Responsibilities:
1. Lead a local AI/ML team with accountability and engagement into a global organization.
2. Mentor and guide team members, fostering an inclusive culture with a growth mindset.
3. Collaborate on setting the technical vision and executing strategic roadmaps to drive AI innovation.
4. Deliver AI/ML projects through our ML development life cycle using Agile methodology. Help transform business requirements into AI/ML specifications, define milestones, and ensure timely delivery.
5. Work with product and business teams to define goals and roadmaps. Maintain alignment with cross-functional stakeholders.
6. Exercise sound technical judgment, anticipate bottlenecks, escalate effectively, and balance business needs versus technical constraints.
7. Design experiments, establish mathematical intuitions, implement algorithms, execute test cases, validate results and productionize highly performant, scalable, trustworthy and often explainable solution.
8. Participate and contribute back to firmwide Machine Learning communities through patenting, publications and speaking engagements.
9. Evaluate and design effective processes and systems to facilitate communication, improve execution, and ensure accountability.
Required qualifications, capabilities, and skills:
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10. Experience as a hands-on practitioner developing production AI/ML solutions.
11. Knowledge and experience in machine learning and artificial intelligence. Ability to set teams up for success in speed and quality, and design effective metrics and hypotheses.
12. Expert in at least one of the following areas: Large Language Models, Natural Language Processing, Knowledge Graph, Reinforcement Learning, Ranking and Recommendation, or Time Series Analysis.
13. Good understanding of Data structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, Statistics.
14. Experience in advanced applied ML areas such as GPU optimization, finetuning, embedding models, inferencing, prompt engineering, AI evaluation, RAG (Similarity Search).
15. Demonstrated expertise in machine learning frameworks: Tensorflow, Pytorch, pyG, Keras, MXNet, Scikit-Learn.
16. Strong programming knowledge of python, spark; Strong grasp on vector operations using numpy, scipy; Strong grasp on distributed computation using Multithreading, Multi GPUs, Dask, Ray, Polars etc.
Preferred qualifications, capabilities and skills
17. Familiarity in AWS Cloud services.
18. Strong people management and team-building skills. Ability to coach and grow talent, foster a healthy engineering culture, and attract/retain talent. Ability to build a diverse, inclusive, and high-performing team.
19. Ability to inspire collaboration among teams composed of both technical and non-technical members. Effective communication, solid negotiation skills, and strong leadership.