We are looking for an experienced Data Scientist with a strong background in Retail & E-commerce Analytics , particularly in integrating offline store data into analytics solutions. The ideal candidate will leverage General Analytics , AI/ML , and other advanced data science techniques to bridge the gap between online and offline retail data, enabling smarter business decisions and enhancing customer experiences across both channels.
Responsibilities:
Offline Store Data Analytics :Analyze and integrate offline store data (sales, foot traffic, inventory, etc.) with online data to provide a holistic view of customer behavior and sales trends.Use AI/ML models to optimize offline store performance by forecasting demand, inventory needs, and staffing levels based on historical data.Analyze the impact of offline marketing campaigns and promotions on in-store foot traffic and sales, using predictive analytics and machine learning.Develop models to predict store performance, taking into account various factors like location, weather, local events, and other external variables.Retail & E-commerce Analytics :
Use machine learning algorithms and statistical analysis to understand and predict consumer behavior, both in-store and online.Build and maintain models for customer segmentation , personalized marketing , and sales forecasting for both e-commerce and brick-and-mortar stores.Identify key metrics for store performance and customer satisfaction, helping management teams optimize strategies for in-store and online experiences.AI/ML Implementation For Retail Optimization :
Apply AI/ML techniques (e.g., classification , regression , clustering , time series forecasting ) to retail data, enabling actionable insights for improving product assortment, pricing, and promotions both online and offline.Develop demand forecasting models for offline stores , ensuring optimal stock levels based on predicted customer needs and sales trends.Use machine learning to enhance inventory management in offline stores by predicting inventory shortages and surplus.Data Integration & Visualization :
Work with large datasets from both offline and online sources to clean, integrate, and analyze the data, ensuring data accuracy and consistency.Develop dashboards and visualizations using tools like Tableau , Power BI , or Google Data Studio to communicate key findings and business insights to stakeholders.Create reports that combine insights from offline and online channels, providing a unified view of retail operations.Campaign Performance Analysis :
Use data science techniques to analyze the effectiveness of offline marketing campaigns and promotions on both in-store and online traffic, conversion rates, and sales.Evaluate customer engagement with offline campaigns, offering insights into how online and offline channels influence each other.Collaboration With Cross-Functional Teams :
Work closely with retail managers, marketing teams, and IT teams to implement data-driven solutions that improve customer experience and business performance.Collaborate with product, marketing, and supply chain teams to optimize the omnichannel strategy , including aligning online and offline inventories and promotions.Key Technical Skills:
Machine Learning & AI :Proficiency in Python or R for building and deploying AI/ML models such as random forests , XGBoost , SVM , and neural networks .Strong experience in applying predictive modeling , regression analysis , and time series forecasting to retail data, including demand forecasting and sales prediction.Familiarity with deep learning techniques (e.g., RNNs , LSTMs ) for more complex data patterns, if relevant.Retail & E-commerce Analytics :
Experience in analyzing point-of-sale (POS) data , foot traffic data , and customer journey data for offline retail stores.Understanding of e-commerce KPIs and how they integrate with offline store performance metrics.Strong knowledge of inventory optimization , supply chain management , and how they relate to both online and offline retail operations.Big Data & Data Integration :
Proficiency in handling and analyzing large datasets from multiple sources (online and offline) using SQL , NoSQL , and cloud platforms like AWS , GCP , or Azure .Ability to work with ETL processes to integrate data from multiple systems, ensuring high-quality data for analysis.Data Visualization & Reporting :
Experience with Tableau , Power BI , Google Data Studio , or other visualization tools to present insights and actionable business recommendations.Strong communication skills to present complex findings to both technical and non-technical stakeholders.Statistical Analysis :
Proficiency in statistical methods for hypothesis testing, segmentation analysis, and measuring the effectiveness of retail strategies and campaigns.Familiarity with advanced statistical techniques, including Bayesian methods , Monte Carlo simulations , and multivariate testing .Desired Qualifications:
Bachelor’s or Master’s degree in Computer Science , Data Science , Statistics , Engineering , or a related field.5+ years of experience in data science or analytics in the retail or e-commerce industry, with a focus on offline store performance .Proven experience in applying AI/ML models to solve real-world business problems, including demand forecasting, personalization, and campaign optimization.Familiarity with offline store data (sales, foot traffic, inventory) and how it integrates with e-commerce platforms .Strong understanding of the retail industry, particularly in optimizing performance across omnichannel environments (online and offline).
