We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorgan Chase within the Consumer and Community Banking, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

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

 

Formal training or certification on software engineering concepts and 5+ years applied experience MS or PhD degree in Computer Science, Statistics, Mathematics or Machine learning related field. 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 qualifications, 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