Applied AI ML Lead
Applied AI ML Lead
Employer Active
Job Alert
You will be updated with latest job alerts via email
Share
Job Alert
You will be updated with latest job alerts via email
Job Description
The Risk Management & Corporate Technology Machine Learning team at JPMorgan Chase is dedicated to addressing complex business challenges through the application of data science and machine learning techniques across Risk Compliance Conduct and Operational Risk. As an Applied AI/ML Engineer on the team you will have the opportunity to explore intricate business problems and apply advanced algorithms to develop test and evaluate AI/ML applications or models for these challenges.
You will leverage the firms extensive data resources from both internal and external sources using Python Spark and AWS among other systems. You are expected to extract business insights from technical results and effectively communicate them to a nontechnical audience.
Job Responsibilities
- Design and architect end to end solutions in AI domain ranging from Anomaly detection Use cases Chat with your at data and using GenAI.
- Proactively develop an understanding of key business problems and processes.
- Execute tasks throughout the model development process including data wrangling/analysis model training testing and selection.
- Generate structured and meaningful insights from data analysis and modelling exercises and present them in an appropriate format according to the audience.
- Collaborate with other data scientists and machine learning engineers to deploy machine learning solutions.
- Conduct adhoc and periodic analysis as required by business stakeholders the model risk function and other groups.
Required qualifications capabilities and skills
- Proven experience postadvanced degree (MS PhD) in a quantitative field (e.g. Data Science Computer Science Applied Mathematics Statistics Econometrics).
- Experience in statistical inference and experimental design (such as probability linear algebra calculus).
- Data wrangling: understanding complex datasets cleaning reshaping and joining messy datasets using Python.
- Practical expertise and work experience with ML projects both supervised and unsupervised.
- Proficient programming skills with Python including libraries such as NumPy pandas and scikitlearn as well as R.
- Understanding and usage of the OpenAI API.
- NLP: tokenization embeddings sentiment analysis basic transformers for textheavy datasets.
- Experience with LLM & Prompt Engineering including tools like LangChain LangGraph and RetrievalAugmented Generation (RAG).
- Experience in anomaly detection techniques algorithms and applications.
- Excellent problemsolving communication (verbal and written) and teamwork skills.
Preferred qualifications capabilities and skills
- Experience with deep learning frameworks such as TensorFlow and PyTorch.
- Experience with big data frameworks with a preference for Databricks.
- Experience with databases including SQL (Oracle Aurora) and Vector DB.
- Familiarity with version control systems such as Bitbucket and GitHub.
- Experience with graph analytics and neural networks.
- Experience working with engineering teams to operationalize machine learning models.
- Familiarity with the financial services industry.
Employment Type
Full-Time
