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.