Associate: Marketing Analytics - Marketing Mix Modelling Job in Mumbai at JP Morgan India

JP Morgan is hiring for the role of Associate: Marketing Analytics!

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
• Analyze modeling dataset to understand trends and seasonality. Check data for outliers and identify data issues. 
• Create media response transformations for all media variables; Pull macroeconomics data from a website. Compile modeling dataset
• Build market mix model from scratch, create base and channel contributions, calculate channel ROIs
• Create optimizations for what-if scenarios. Create necessary charts (including trend and waterfall) and tables for presentations
• 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 with experience in building market mix models 
• Understanding of media and advertising variables, response curves
• Experience using computer languages (Python, Pyspark, Google Analytics, .) 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