VP - Applied AI ML Lead

J.P. Morgan Asset & Wealth Management delivers industry-leading investment management and private banking solutions. Asset Management provides individuals, advisors and institutions with strategies and expertise that span the full spectrum of asset classes through our global network of investment professionals. Wealth Management helps individuals, families and foundations take a more intentional approach to their wealth or finances to better define, focus and realize their goals.​

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. 

As an Applied AI/ML Vice President within Asset & Wealth Management, you will utilize your quantitative, data science, and analytical skills to tackle complex problems. Your role will involve collaborating with various teams to design, develop, evaluate, and execute data science and analytical solutions, all while maintaining a deep functional understanding of the business problem at hand. Your responsibilities will also include data wrangling, data analysis, and modeling, which encompasses model selection and the creation of swift, applicable modeling solutions.

Job responsibilities:

• Engages with stakeholders and understanding business requirements,
• Develops AI/ML solutions to address impactful business needs,
• Works with other team members to productionize end-to-end AI/ML solutions,
• Engages in research and development of innovative relevant solutions,
• Coaches other AI/ML team members towards both personal and professional success,
• Collaborates across teams to attain the mission and vision of the team and the firm

Required qualifications, capabilities and skills:

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• Advanced degree in analytical field (e.g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis, Operations Research)
• Experience in the application of AI/ML to a relevant field.
• Demonstrated practical experience in machine learning techniques, supervised, unsupervised, and semi-supervised.
• Strong experience in natural language processing (NLP) and its applications.
• Solid coding level in Python, with experience in leveraging libraries, like Tensorflow, Keras, Pytorch, Scikit-learn, or others.
• Previous experience in working on Spark, Hive, or SQL.

Preferred Qualifications, capabilities and skills:

• Demonstrated practical experience in application of LLMs for solving business problems.
• Financial service background.
• PhD in one of the above disciplines.