JP Morgan Chase - Risk Associate - Quantitative Modeling - Consumer & Community Banking - SWAT

Quantitative modeling is responsible for development, maintenance and management of quantitative models that are used for most critical decisions within CCB Risk. With models that are aligned to line of business- Cards, Business Banking, Auto, Homelending as well as for horizontal areas like fraud management, capital models, stress testing, CCAR etc. Quant Modelers regularly interact with variety of stakeholders including but not limited to LOB teams, MRGR, Fair Lending, Legal, Implementation,Tech teams etc.-

Primary Responsibilities:

- Own end-to-end risk model diagnostic efforts within Core Modeling using advanced statistical/mathematical techniques like regression, XG Boost, Nueral nets, SVM or other traditional modeling/ machne learning methods.

- Liasoning with risk development partners like MRGR, Fair Lending, Legal, Technology and LOB Business Stakeholders

- Efficiently design and produce models following procedures for model development, validation, and reporting

- Provide support for model implementation, performance monitoring and calibration.

- Expected to work on multiple projects with modeling teams in other locations, to ensure high quality model development standards,reviews and re-reviews, model monitoring, enhanced model usage support etc in compliance with firm's Estimation policies and procedures. Ensure right development and usage of models owned.

Qualification:

- 3+ years statistical model development/ Machine learning model development related data experience in a deeply technical role in the financial services industry or Fintechs dealing with advanced analytical or machine learning methods

- 3+ years SAS experience; well versed in SAS/Base, SAS/STAT, SAS/Macro, and data-mining procedures. SAS Certification preferred. OR

- 3+ years experience inPython, Spark, Hive, Scala, Big Data, Hadoop, Keras, Scikitlearn etc.

- Experience in developing, implementing and testing traditional and Machine Learning driven risk models within financial institutions with strong regulatoy oversight and tight timelines.

- Ability to deliver high-quality results under tight deadlines and be comfortable with the manipulation, analysis, and summarization of large quantities of data.

- Ability to make contributions to the group's knowledge base by proposing new and creative ways for approaching analytic problems and project design.

- Strong analytical, technical and statistical skills.

- Proficient with advanced analytical tools and techniques

- Hands-on experience in ML techniques a plus (Tensorflow, Keras, Scikitlearn, Seaborn etc).

- Ability to multi-task and present quantitative information succintly is required.

JPMorgan Chase & Co., one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. In accordance with applicable law, we make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as any mental health or physical disability needs.

Note : For your candidature to be considered on this job, you need to apply necessarily on the company's redirected page of this job. Please make sure you apply on the redirected page as well.