They must be comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes. The ideal candidate will have at least 4+ years of hands on experience in building Data Science models in projects involving large and complex data in retail ecosystem.
Responsibilities:
- Work with internal stakeholders throughout the organization to identify opportunities for solving various business use cases leveraging company data.
- Building and deploying Markdown Optimization model to help the Merchandizing team to optimize product sales.
- Building and deploying sophisticated Demand forecasting models to increase accuracy of supply decisions.
- 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.
- Developing detailed store performance analysis to optimize staff allocation and product allocation.
- Building Market Basket algorithms to drive successful cross-category campaigns, product bundling, stock handling and item arrangement patterns.
- Building API based solutions for supporting or recommending store opening decisions.
- 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.
- Coordinate with different functional teams to implement models and monitor outcomes.
- Develop processes and tools to monitor and analyze model performance and data accuracy.
- Collaborating closely with the Data Engineering and BI team to build a seamless workflow.