JP Morgan Chase - Associate - Fraud Data Science - Risk Management

The Fraud Data Science team uses advanced analytical techniques to mitigate fraud across various payment channels such as Digital Transactions including QuickPay / Quick Deposit, Credit Card, Debit Card and Checks. The team works closely with business analysts, product owner and operations to find tangible opportunities to prevent fraud, enhance customer experience and provide insights which will allow assessing the risk of portfolio in several dimensions. The person will need to work on complex problems using Big Data, and Machine Learning. Develop analytical strategies that can significantly change how fraud is managed and set the path for future

The person will own the project end to end. He should be able to identify the potential value of data (structured/unstructured) by pulling the data from different sources on the Big data environment, validating and preparing the data for analysis, to be able to use any ML algorithms for the purpose of analysis and deliver actionable insights by doing quick Proof of Concepts, pilots and incremental value outcomes.

Requirements :

Partner with various strategy, product, and operational teams to drive multiple analyses on fraud risk across different products. 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:

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

- Proactively seeks, finds and recommends opportunities to improve underlying processes.

- Use of advanced analytical tools and platforms (including Hadoop) to drive multiple analytical proof of concepts

- Research, design, implement, and evaluate machine learning approaches and models

- 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

- Provides accurate and concise results and presents findings, recommendations and presentations to Management.

- Collaborate across cross functional teams to knowledge share and develop broader insights into fraud and customer impacts

- Communicate findings and obstacles to stakeholders to help drive the delivery to market

Qualifications & Skills Required:

- Minimum of 4 years of related experience with prior experience as a data scientist or a related role

- Bachelor's degree in a quantitative discipline; Math, Finance, Statistics, Economics Engineering or equivalent work/training is required; advanced degree is preferred

- Expertise in theory and practice of Statistics, Empirical Data Analysis and Machine Learning;

- Experience and in-depth knowledge of Python,SQL, and other modern programming languages; Knowledge of Spark and Scala are a strong plus

- Experience in practical data processing, data mining, text mining and information retrieval tasks

- Strong communication and interpersonal skills, ability to interact with individuals across departments / functions and with senior-level individuals

- Desire to use modern technologies as a disruptive influence within the Banking domain

- Great communication skills, team player, self-starter, strong work ethic

- Highly proficient in Microsoft Office suite of products