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JOB DESCRIPTION
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.
Qualifications:
. Strong problem-solving skills with an emphasis on resolving real life business use cases.
. Working experience in Retail sector is a must. Understanding 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.
