JPMorgan Chase & Co. (NYSE: JPM) is a leading global financial services firm with assets of $2 trillion and operations in more than 60 countries. The firm is a leader in investment banking, financial services for consumers, small business and commercial banking, financial transaction processing, asset management, and private equity.  - Chase Consumer & Community Banking serves nearly 66 million consumers and 4 million small businesses with a broad range of financial services through our 137,000 employees. Consumer & Community Banking Risk Management partners with each CCB sub-line of business to identify, assess, prioritize and remediate risk. Our Risk Management professionals work directly with Consumer Banking, Business Banking, Auto/Student Loan, Card and Commerce Services, Chase Wealth Management and Mortgage Banking to minimize, monitor and control the probability of risk events and mitigate the impact of risk events that do occur.  - The CCB Risk Core Modeling Data Scientist will be a key individual contributor on the Fraud Modeling team that is responsible for developing and implementing best-in-class fraud prevention and detection models and analytical tools.   - The CCB Fraud Modeling team is an analytical center of excellence to all fraud risk managers and operations across the bank. The team provides diverse models and analytical tools used to identify potentially fraudulent transactions across different lines of business (card, retail, auto, merchant services). In this role, you will be the analytical expert for identifying and retooling suitable machine learning algorithms that can enhance the fraud risk ranking of particular transactions and/or applications for new products. This includes a balance of feature engineering, feature selection, and developing and training machine learning algorithms using cutting edge technology to extract predictive models/patterns from data gathered for hundreds of millions transactions. Your expertise and insights will help us effectively utilize big data platforms, data assets, and analytical capabilities to control fraud loss and improve customer experience.  Your key responsibilities will include :   - Feature engineering  - Feature selection for traditional GLM models (e.g Lasso, ElasticNet, etc) and machine learning models  - Collaborate with fraud prevention/detection strategy teams and operations to understand business needs, data generating process, system capability, and potential impact of models.  - Design machine learning algorithms that can be used to improve the fraud prevention/detection scores  - Source data and apply feature engineering for model development and deployment   - Provide requirements and assist Information Technology for model deployment  - Document model solutions and address questions/concerns from model risk and control partners.  Qualifications & Personal skills :  - Expert in generalized linear models, unsupervised and supervised machine learning algorithms   - Demonstrated experience with Big Data tools like Hadoop & Spark  - Demonstrated proficiency in advanced analytical languages such as R, Python, Scala, SAS  - Experience with traditional database/system languages (e.g. SAS, SQL, etc.) to collaborate with other data analysts/systems  - Ph.D./ Masters Mathematics, Statistics, Economics, Computer Science, or related field  - Experience with implementing scalable machine learning/data mining algorithms making use of distributed/parallel processing  - Experience with model development in Financial Industry is preferred

Mumbai

JP Morgan Chase - Data Scientist - Core Modeling 

JP Morgan Chase - Data Scientist - Core Modeling  ,Job Description for JP Morgan Chase - Data Scientist - Core Modeling  in Citibank in  Mumbai for (2-8) years of experience. Apply Now!

Job Description for JP Morgan Chase - Data Scientist - Core Modeling  in Citibank in  Mumbai for (2-8) years of experience. Apply Now!