Applied AI ML Lead

Job Description
As an Applied AI ML Lead within the Digital Intelligence team at JPMorgan, collaborate with all lines of business and functions to deliver software solutions. Experiment, develop, and productionize high-quality machine learning models, services, and platforms to make a significant impact on technology and business. Design and implement highly scalable and reliable data processing pipelines. Perform analysis and insights to promote and optimize business results. Contribute to a transformative journey and make a substantial impact on a wide range of customer products.
Job Responsibilities:

• Design and implement end-to-end machine learning solutions for the production environment to solve complex problems related to personalized financial services in retail and digital banking.
• Work closely with other Machine Learning practitioners and cross-functional teams to translate business requirements into technical solutions and drive innovation in our banking products and services.
• Collaborate with Machine Learning engineers, product managers, key business stakeholders, engineering, and platform partners to deploy projects that deliver cutting-edge machine learning-driven digital solutions.
• Write code to create machine learning experimentation pipelines and design feature engineering pipelines to push to feature stores.
• Collaborate with data engineers and product analysts to preprocess and analyze large datasets from multiple sources.
• Execute experiments and validations at scale, and review results with Lead and Products.
• Create model serving pipelines that meet consumption SLAs and write production-grade code for both training and inference functions.
• Collaborate with MLOps engineers to develop and test training and inference applications under the production architecture blueprint, integrating with upstream and downstream applications, while also registering model artifacts, maintaining code repositories, and preparing for CI/CD execution and post-production monitoring.
• Drive end-to-end system architecture in collaboration with ML, MLOps, and Architecture leads.
• Communicate and collaborate with Platform and Engineering partners to bring in the latest advancements to improve the scale, consistency, reliability, and trustworthiness of the ML solutions
• Mentor junior Machine Learning associates in delivering successful projects and building careers, while also contributing to firm-wide ML communities through patenting, publications, and speaking engagements.

Required qualifications, capabilities and skills:

• BS, MS, or PhD degree in Computer Science, Statistics, Mathematics, or a related field in Machine Learning.
• Expert proficiency in implementing ML models in at least one of the following areas: Natural Language Processing, Knowledge Graphs, Computer Vision, Speech Recognition, Reinforcement Learning, Ranking and Recommendation, or Time Series Analysis.
• Foundational knowledge in data structures, algorithms, machine learning, data mining, information retrieval, and statistics.
• Demonstrated expertise in machine learning frameworks such as TensorFlow, PyTorch, PyG, Keras, MXNet, and Scikit-Learn.
• Expert programming knowledge in Python and Spark; expert coding knowledge of vector operations using NumPy and SciPy.
• Coding knowledge in distributed computation using multithreading, multi-GPUs, Dask, Ray, Polars, etc.
• Strong analytical and critical thinking skills for problem-solving.
• Excellent written and oral communication skills, along with demonstrated teamwork abilities.
• Demonstrated ability to clearly communicate complex technical concepts to both technical and non-technical audiences.
• Experience working with interdisciplinary teams and collaborating with other researchers, engineers, and stakeholders.

Preferred qualifications, capabilities and skills:

• Familiarity with distributed data and feature engineering using popular cloud services like AWS EMR.
• Exposure to large-scale training, validation, and testing experiments.
• Experience with cloud Machine Learning services in AWS, such as SageMaker.
• Understanding of container technologies like Docker and ECS.
• Knowledge of Kubernetes-based platforms for training or inferencing.

About Us
JPMorgan Chase & Co., one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
About the Team
Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions - all while ranking first in customer satisfaction.
The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.