JP Morgan Chase - Associate - Fraud Risk Data Science

Associate - Fraud Risk Data Science

Description:

In this role, you will work in a team of data scientists responsible for using advanced Machine Learning techniques to mitigate fraud across digital and non-digital payment channels. You will build in-house models and ML driven algorithmic processes to identify fraud trends which are hard to catch with conventional means. You will get to work closely with business analysts, product owners and operations to find tangible opportunities to prevent fraud, enhance customer experience and provide insights which will allow us to assess the risk of portfolio in several dimensions.

Our culture in Risk Management and Compliance is all about thinking outside the box, challenging the status quo and striving to be best-in-class. You help the firm grow its business in a responsible way by anticipating new and emerging risks, and using your expert judgement to solve real-world challenges that impact our company, customers and communities.

You will help build a foundation of state-of-the-art technical and scientific capabilities to support a number of ongoing and planned data analytics projects. We build partnerships with stakeholders in this role to develop analytical solutions by the lines of business. You will excel at creative thinking and problem solving, be self-motivated, confident and ready to work in a fast-paced energetic environment.

Job responsibilities:

- Build an in-depth understanding of the problem domain and available data assets

- Research, design, implement, and evaluate Machine Learning approaches and analytical tools

- Perform ad-hoc exploratory statistics and data mining tasks on diverse datasets from small scale to "big data"

- Investigate data visualization and summarization techniques for conveying key findings

- Partner with and communicate findings to stakeholders to help drive the delivery to market

Required qualifications, capabilities, and skills:

- 3-5 years of experience in Data Science/ Machine Learning Modeling, preferably in risk management within Banking Industry

- Bachelor's Degree or Master's Degree in Mathematics, Statistics, Computer Science, Physical Science, Engineering, or other quantitative discipline;

- Fundamental understanding of probability and statistics and experience in ML algorithm development using large scale data

Preferred Qualifications, Capabilities, and Skills:

- Experience in Python and/or PySpark is must. Experience with AWS environment is preferred.

- Experience in Graph database is an added advantage; although not mandatory.

- Desire to use modern technologies as a disruptive influence for solving large scale business problem

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. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

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.