Asset Management: Client Data Science - Associate Job in Mumbai at JP Morgan India

JP Morgan is hiring for the role of Asset Management: Client Data Science - Associate!

Responsibilities of the Candidate:

• Partnering with  business stakeholders to identify opportunities for leveraging advanced analytics. Leverage the power of AI/ML to solve complex business problems
• Embed analytics process in organizational decision making: descriptive, diagnostic and predictive and prescriptive analytics to derive better business outcomes
• Building analytical frameworks for marketing measurement – A/B testing, campaign ROI(return on investment) , cost of acquisition modeling, lifetime value, etc.
• Explore and mine data to develop a deep understanding of client behavior, segmentation and engagement models, market opportunity sizing and wallet share evaluations, build recommendation engines and personalization frameworks, build predictive call to action solutions, measurement analytics  
• Contributing to and enhancing data models and feature stores. Leveraging machine learning models and techniques to target opportunities and optimize processes
• Developing tools and solutions to measure model performance over time and fostering a data-driven, innovative culture
• Acting as a proxy product-owner and consultant to provide optimal data solutions for various business problem statements / pain-points and goals

Requirements:

• MBA or advanced degree in Statistics, Math, Engineering or other quantitative-focused field 
• Experience in an analytics role, financial services/marketing.
• Experience using computer languages (Python, Pyspark, Google Analytics, etc.) to manipulate and analyze large datasets. Story-telling acumen using Tableau, power-point etc. tools
• Sound knowledge of SQL to collate data from various sources
• Experience using machine learning libraries in Python (Numpy, Pandas, Sci-kit learn, tensorflow, 
• Knowledge of variety of machine learning algorithms (linear regression, logistic regression, SVMs, Tree-based models, neural networks, etc.) and techniques (cross validation, feature selection approaches, hyper parameter optimization, missing data imputation etc.)
• Strong problem-solving skills and excellent communicator (written and verbal) with strong interpersonal skills