Akash Kulkarni
About Akash Kulkarni
Akash Kulkarni is a Lead Analyst at Credit Karma, specializing in analytics with over 10 years of experience. He holds a Master of Science in Business Analytics from the University of Cincinnati and has previously worked at LendingTree and Tredence.
Work at Credit Karma
Akash Kulkarni has been serving as a Lead Analyst at Credit Karma since 2018. In this role, he focuses on driving marketing efficiency through model-driven approaches within the Recommendation Analytics team. His responsibilities include measuring the incrementality of various experiments and reporting findings to the business and executive teams. He collaborates across functions, working closely with Marketing, Engineering, Data Science/Machine Learning, and Product Management teams. His projects involve personalization of marketing communication frequency, forecasting and simulation, and engagement-revenue tradeoff.
Education and Expertise
Akash Kulkarni holds a Master of Science (M.S.) in Business Analytics from the University of Cincinnati. He also earned a Bachelor of Engineering (B.E.) in Mechanical Engineering from Visvesvaraya Technological University. His academic background provides a strong foundation for his specialization in segmentation and clustering, experiment design and measurement, and market mix modeling. He has over 10 years of experience in analytics, focusing on experimentation, predictive analytics, and recommendation systems.
Professional Background
Before joining Credit Karma, Akash Kulkarni worked at LendingTree as a Data Scientist from 2016 to 2018. Prior to that, he was an Associate at Tredence from 2014 to 2016. His earlier experience includes a role as a Business Analyst at Mu Sigma from 2011 to 2013, and he completed summer internships at Continental in 2009 and Larsen & Toubro in 2010. His diverse experience spans various roles and industries, contributing to his expertise in analytics.
Technical Skills and Tools
Akash Kulkarni is proficient in several analytical tools and programming languages, including Python, SQL, SAS, R, SPSS, Tableau, Looker, and Spotfire. His technical skills encompass expertise in linear and logistic regression, random forest, and tree classifiers. This skill set enables him to effectively analyze data and derive insights that inform business strategies and decision-making.