Job Description

Join JPMorgan Corporate Investment Bank's industry-leading data analytics team, where you'll combine cutting-edge machine learning techniques with unique data assets to optimize business decisions. As an Applied AI & Machine Learning Associate, you'll advance financial applications from business intelligence to predictive models and automated decision-making, working closely with Digital & Platform Services Operations.





As an Applied AI ML Senior Associate in the Commercial & Investment Bank, you will apply data analytics techniques from traditional statistics and machine learning to various datasets, aiming to answer questions relevant to Operations. Collaborate with Digital & Platform Services Operations teams and other stakeholders to support the Corporate & Investment Bank and its partners.

Job Responsibilities:

Research and develop innovative ML-based solutions to address Operations' most challenging problems. Build robust Data Science capabilities scalable across multiple business use cases. Collaborate with the software engineering team to design and deploy Machine Learning services integrated with strategic systems. Research and analyze datasets using a variety of statistical and machine learning techniques. Communicate AI capabilities and results to both technical and non-technical audiences. Document approaches, techniques, and processes followed.

Required Qualifications, Capabilities, and Skills:

Master's or PhD degree in a quantitative or computational discipline. Hands-on experience developing and deploying Data Science and ML capabilities in production at scale. Strong Python development and debugging skills. Ability to work both individually and collaboratively with others. Curiosity, attention to detail, and interest in complex analytical problems. Results-driven mindset and client focus. Ability to work in agile cross-functional teams.

Preferred Qualifications, Capabilities, and Skills:

Experience with Natural Language Processing (NLP). Ability to design intrinsic and extrinsic evaluations of a model's performance aligned with business goals. Ability to work with non-specialists in a partnership model, conveying information clearly and creating trust with stakeholders. Experience with machine learning frameworks (e.g., PyTorch, TensorFlow) and data science packages (e.g., Scikit-Learn, NumPy, SciPy, Pandas, statsmodels). * Experience with big-data technologies such as Spark, SageMaker, etc.