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JOB DESCRIPTION
Key Responsibilities:
· Work with internal stakeholders in the CRM team to identify opportunities for solving various business use cases leveraging company data.
· Developing Price Sensitivity analysis for optimizing footfall, conversion and turnover during promotions.
· Building and deploying Churn Prediction and Customer Lifetime Value Model to help the brands in better retention of Loyal and potential customers.
· Building RFM model and various customer profiling based on attributes such as transactional behavior, affluency and cross-brand engagement within the company ecosystem etc.
· Building Market Basket algorithms to drive successful cross-category campaigns, product bundling, stock handling and item arrangement patterns.
· Creating customer cohorts based on tracked footprint of a customer across various internal brands.
· Assess effectiveness and accuracy of new data sources and data gathering techniques and also raise request for relevant data attributes to successfully build and deliver an analytical project.
· Use analytical approach to drive customer loyalty, new acquisitions, better retention and cross-brand engagement.
· Support the organization in driving promotional / non-promotional marketing campaigns through analytics driven approach and measuring campaign effectiveness through scientific method of calculating incremental ROI.
· Develop processes and tools to monitor and analyze model performance and data accuracy.
Qualifications:
· Strong problem-solving skills with an emphasis on resolving real life business use cases.
· Working experience in Retail sector and understanding of fashion retail is a plus.
· Proficient in understanding and implementing machine learning algorithms.
· Working experience in data mining techniques like Logistic Regression, Multivariate Analysis, Decision Tree, Random Forest, K-means, Support Vector method, Neural Network, ARIMA, GARCH Model, Text Mining, NLP etc. with their real-world advantages/drawbacks.
· Proficient scripting in programming language like Python, PySpark, Scala, SQL etc.
· Exposure to using tools like SAS Enterprise Miner, IBM Watson Studio, Microsoft Azure ML Studio, DataRobot, DataIku are preferred.
· A drive and ability to quickly learn and master new tools and technologies is a must.
· Excellent written and verbal communication skills for coordinating across Internal and external stakeholders.
· 4-5 years of experience with core Data Science Exposer and building statistical models and a degree in Statistics, Mathematics, Computer Science or another quantitative field.
· Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, MySQL, etc.
· Knowledge working with various APIs. Exposure to using Google APIs like Geocoding, Reverse Geocoding, Places etc.
· Exposure to using Web Analytics tools like Google Analytics, Adobe Analytics etc.
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