Job
Description
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). 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