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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.

Apparel Group