Applied AI ML Lead

Job Description
We're seeking top talents for our AI engineering team to develop high-quality machine learning models, services, and scalable data processing pipelines. Candidates should have a strong computer science background and be ready to handle end-to-end projects, focusing on engineering.
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

• Research, develop, and implement machine learning algorithms to solve complex problems related to personalized financial services in retail and digital banking domains.
• Work closely with cross-functional teams to translate business requirements into technical solutions and drive innovation in banking products and services.
• Collaborate with product managers, key business stakeholders, engineering, and platform partners to lead challenging projects that deliver cutting-edge machine learning-driven digital solutions.
• Conduct research to develop state-of-the-art machine learning algorithms and models tailored to financial applications in personalization and recommendation spaces.
• Design experiments, establish mathematical intuitions, implement algorithms, execute test cases, validate results, and productionize highly performant, scalable, trustworthy, and often explainable solutions.
• Collaborate with data engineers and product analysts to preprocess and analyze large datasets from multiple sources.
• Stay up-to-date with the latest publications in relevant Machine Learning domains and find applications for the same in your problem spaces for improved outcomes.
• Communicate findings and insights to stakeholders through presentations, reports, and visualizations.
• Work with regulatory and compliance teams to ensure that machine learning models adhere to standards and regulations.
• Mentor Junior Machine Learning associates in delivering successful projects and building successful careers in the firm.
• Participate and contribute back to firm-wide Machine Learning communities through patenting, publications, and speaking engagements.

Required qualifications, capabilities and skills

• Expert in at least one of the following areas: Natural Language Processing, Knowledge Graph, Computer Vision, Speech Recognition, Reinforcement Learning, Ranking and Recommendation, or Time Series Analysis.
• Deep knowledge in Data structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, Statistics.
• Demonstrated expertise in machine learning frameworks: Tensorflow, Pytorch, pyG, Keras, MXNet, Scikit-Learn.
• 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.
• Strong analytical and critical thinking skills for problem solving.
• Excellent written and oral communication along with demonstrated teamwork skills.
• Demonstrated ability to clearly communicate complex technical concepts to both technical and non-technical audiences
• Experience in working in interdisciplinary teams and collaborating with other researchers, engineers, and stakeholders.
• A strong desire to stay updated with the latest advancements in the field and continuously improve one's skills

Preferred qualification, capabilities and skills

• Deep hands-on experience with real-world ML projects, either through academic research, internships, or industry roles.
• Experience with distributed data/feature engineering using popular cloud services like AWS EMR
• Experience with large scale training, validation and testing experiments.
• Experience with cloud Machine Learning services in AWS like Sagemaker.
• Experience with Container technology like Docker, ECS etc.
• Experience with Kubernetes based platform for Training or Inferencing.
• Contributions to open-source ML projects can be a plus.
• Participation in ML competitions (e.g., Kaggle) and hackathons demonstrating practical skills and problem-solving abilities.
• Understanding of how ML can be applied to various domains like healthcare, finance, robotics, etc.