Marriam Arshad

MARRIAM ARSHAD DATA SCIENTIST | AUTOMATION SPECAILIST PROFILE CONTACT Address: lahore, Pakistan Results-driven Data Scientist and Automation Specialist with expertise in CRM, AI, and e-commerce operations. Skilled in streamlining workflows, driving customer engagement, and delivering data-driven solutions. Seeking to leverage technical and leadership strengths in a Customer Service Manager role to enhance efficiency and customer satisfaction. EDUCATION Bachelor of Computer Science Punjab University EXPERIENCE SOFT SKILLS Teamwork Time Management Leadership Effective Communication Data Scientist Alzaaki Technologies May 2022- Feb 2025 Developed an AI-powered product recommendation engine using Python, TensorFlow, and Keras, boosting CTR and average order value. Conducted data analysis and customer segmentation to personalize marketing campaigns and improve targeting accuracy. Built predictive models and optimized machine learning pipelines for e-commerce operations. Critical Thinking PROJECTS TECH SKILLS Python Data Analytics Data Science Data Structures Automation SQL, MySQL Machine Learning Web Development Automated CRM Workflow Integration Designed and implemented automated workflows for lead management and customer engagement using CRM platforms. Integrated multi-channel communication (email, SMS, WhatsApp) and automated pipeline updates, reducing manual effort by 40% and improving response time. Technologies Used: GoHighLevel, Zapier, HubSpot, Airtable End-to-End Marketing Automation Pipeline Implemented a fully automated marketing pipeline integrating customer segmentation, campaign scheduling, and performance tracking. Enabled real-time analytics and A/B testing for targeted campaigns, increasing conversion rates by 25%. Technologies Used: Make , Google Analytics, Python, SQL, Trello AI-Powered Product Recommendation Engine LANGUAGES English Urdu Developed a recommendation system for an online retail platform to personalize product suggestions based on customer browsing and purchase history. Improved click-through rate and average order value through collaborative filtering and deep learning models. Technologies Used: Python, TensorFlow, Keras, SQL, Tableau