What are the responsibilities and job description for the Applied AI/ML Assiociate position at JPMorgan Chase?
Our team is responsible for pushing innovations in the Payments space, which is a cornerstone of JPMorgan Chase business. Payments are at the center of the global economy, connecting businesses and consumers around the world. In recent years, the payments industry has experienced rapid innovation with new technologies entering the market. Working in payments ML means being at the forefront of these changes and a potential for a meaningful and lasting impact on global finance.
As a Machine Learning Data Scientist, you will be responsible for developing, testing, containerization and deployment of machine learning applications and models to cloud infrastructure. You will work closely with other team members to develop modular and scalable code using object-oriented programming concepts in Python, and collaborate with them using version control systems.
Job responsibilities:
- Actively work to gain a deep understanding of intricate business challenges and processes, and identify opportunities for AI and ML solutions.
- Develop innovative ML-based solutions to address Operations' most challenging problems.
- Collaborate with business partners to drive data-led transformations of the businesses.
- 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
- Bachelor’s with 3 years experience or Master’s degree with 1 year experience in a quantitative field (e.g., Data Science, Computer Science, Applied Mathematics, Statistics, Econometrics).
- 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.
Excellent solution ideation, problem solving, communication (verbal and written), and teamwork skills.
- 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.
- Experience with machine learning frameworks (e.g., PyTorch, TensorFlow), data science packages (e.g., Scikit-Learn, NumPy, SciPy, Pandas, statsmodels) and GenAI toolkit.