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