Marketing & Business Analytics Professional
Carnegie Mellon MSBA graduate with proven expertise in marketing analytics, data science, and AI-driven solutions.
Python, R, SQL, Machine Learning, LLMs, AI
Deloitte (Capstone), Publicis Global Delivery
β’ MS in Business Analytics, Carnegie Mellon University
β’ Bachelor of Commerce, Narsee Monjee College of Commerce and Economics
Two-time "Achiever" awardee, recognized by CEO at Publicis
I'm a business and marketing analytics professional with a Master of Science in Business Analytics from Carnegie Mellon University's Tepper School of Business. I bring a unique blend of technical expertise in data science and machine learning, paired with hands-on experience in marketing analytics and process optimization.
At organizations like Publicis, I've led high-impact initiatives that enhanced ad spend efficiency, improved customer acquisition strategies, and automated workflowsβunlocking measurable business value. I specialize in turning complex data into actionable insights that inform strategy and drive performance.
I'm passionate about using AI and advanced analytics to solve real-world business challenges. My approach combines analytical rigor, strategic thinking, and strong communication to deliver data-driven solutions that align with business goals and drive sustainable growth.
Master of Science in Business Analytics (Merit Scholarship)
GPA: 4.00/4.00 | May 2025
Leadership: Student Leadership Council β Operations Officer and GSA Representative
Bachelor of Commerce (Financial Accounting and Auditing)
GPA: 3.82/4.00 | May 2021
Leadership: Performing Arts Association β Marketing & Finance Head; Finance and Investment Cell β Elected Director
Simulated Consumer Testing with AI
Associate Manager
Ad Operations Analyst
Analyst
Developed predictive logistic regression and decision tree models in R to optimize a bank's telemarketing campaign, improving customer targeting and achieving a 90% cost reduction and 2.5x increase in net profit.
View Details βDesigned a churn prediction model in Python and implemented a proactive retention strategy using an Excel simulator to optimize offers for at-risk mobile customers, improving LTV by $4.39M and achieving 757% ROI.
View Details βUtilized LDA and Euclidean distance in Python to cluster 1,100+ films by topic and identify optimal 2014 release date for The Maze Runner, reducing direct-release competition by 23%.
View Details βApplied k-means clustering in R on demographic and psychographic data to segment small-car buyers and identify high-MQL clusters for Ford Ka, boosting alignment with trend- and value-driven consumers.
View Details βaharlalk@tepper.cmu.edu