Aashutosh Bhardwaj

Working on the Tapestry account, a digital platform managing multiple luxurious brand sites including Coach, Kate Spade, and Stuart Weitzman.

Major Implementations:

• Apple Pay Integration - Spearheaded the integration of Apple Pay on PDP pages, enhancing the checkout process by providing a seamless and secure payment option.
• Amazon Pay Integration: Implemented Amazon Pay to offer an additional, secure, and fast payment solution for customers, improving overall site conversion rates.
• Real-Time Inventory: Developed and integrated real-time inventory tracking, ensuring product availability information is accurate across all brand sites, leading to better customer satisfaction and inventory management.
• Single Page Checkout: Played a key role in optimizing the checkout process by implementing a single-page checkout experience, reducing cart abandonment rates, and improving user experience.
  A/B Testing: Conducted multiple A/B tests to optimize user experience and drive conversion improvements based on data-driven insights.
• Team Collaboration: Worked across various tribe teams, including BUY, Browse, and L3 Manage Services, ensuring cross-functional collaboration and efficient resolution of production defects. Provided L3 support for production issues, ensuring timely and optimal solutions to enhance site reliability and performance.
• Full-Stack Development: Engaged in both front-end and back-end development tasks using various technologies, ensuring a robust and seamless customer experience across multiple luxury brand websites.

Technologies: Salesforce Commerce Cloud (SFCC), SFMC, JavaScript, Node.js, RESTful APIs, OCAPI, Postman, Git, Jenkins.

• Developed machine learning models and algorithms for predictive analytics.
• Conducted data pre-processing operations such as feature engineering, normalization, and imputation.
• Constructed supervised and unsupervised learning models using Python libraries like Scikit-Learn, TensorFlow, and Keras.
• Utilized a variety of techniques including linear regression, logistic regression, decision tree, random forest, gradient boosting, and neural networks.

• Attended weekly team meetings and provided feedback on projects.
• Developed programs to automate processes and improve user experience.
• Integrated software components into existing systems.

• Analyzed user requirements and developed software solutions accordingly with Python.
• Collaborated with other developers to design efficient software architectures utilizing Python programming language.