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), data science packages (e.g., Scikit-Learn, NumPy, SciPy, Pandas, statsmodels) and GenAI toolkit
• Experience in ML Ops