Job Description

Job Overview We are looking for a Data Scientist to join our growing Data Team to support our internal stakeholders such as brand marketing teams, Business Decision makers, Sales and Merchandizing team etc. with data driven insights and products. This hire will be responsible for building various analytical models to optimize decision making within the organization across various departments. He/she must have strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms and creating/running simulations. They must have a proven ability to drive business results with their data-based insights. 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 7+ 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. 7-10 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. Working experience in building analytical projects and models on Merchandizing Analytics like Demand Forecasting, Price Sensitivity and Markdown Optimization is required. Job Type: Full-time