Applied AI ML Lead

In ISDA at JPMorgan Chase, our role involves solving challenging business problems through data science and machine learning techniques across all of Corporate technology and the supported Corporate Functions.

corporate functions, including Compliance, Finance, CAO, Audit and Legal.

As a Applied AI ML Lead within our banking solutions team, you will be responsible for constructing robust and scalable solutions for various banking domains.

Your role will also involve deploying and integrating these services into our existing business workflow.

This position offers an exciting opportunity to work on data-promotion analytical solutions and significantly impact the business processes of a leading global bank.

Job Responsibilities:


* Builds relationships with internal teams and clients and is passionate about unlocking value using AI and Machine learning


* Demonstrates intellectual curiosity for solving difficult problems


* Is a self-starter with technical knowledge and a hands on approach to using our data assets both efficiently and effectively


* Develop approaches for understanding each individual client and their behavior to deliver highly impactful ML models


* Develop, plan and execute analytical projects as an individual contributor and in teams.


* Lead the "productionzation" analytical insights and machine learning models in collaboration with product managers, end users, developers, and other stakeholders to integrate data discoveries and processes into operational capabilities

Required Qualifications, Capabilities and skills :


* Master's Degree or PhD in computer science, statistics, or other quantitative fields


* 2+ years relevant experience.

Expertise in data mining, quantitative research techniques, theories, principles, and practices.

Understanding of statistics, optimization and machine learning methodologies


* Experience in : SQL; Python; AWS or other cloud infrastructure;


* Working with relational and non-relational databases; ML techniques including: regression, classification, time series, clustering, deep learning, hypothesis testing, cross validation, feature selection and feature extraction; NLP techniques including: topic modeling, entity extraction, summarization, and sentiment analysis;


* Working with machine learning and natural language processing libraries such as: scikit-learn, tensor flow, keras, numpy, pandas, CoreNLP, spacY, nltk, pyTorch, NetworkX, or genism; and Building data pipelines and deploying ML models and data visualization techniques.


* Analytical thought leader - You can define the analytical agenda for projects, frame ambiguous business questions into analytical plans (e.g., assess data needs, source files, prepare data, create new features, evaluate quality, etc.), and execute.


* Leadership - Primary focus of building something of significance.

Willingness to roll up sleeves and do whatever it takes

JPMorgan Chase & Co., one of the olde...