I have 2 years of experience working in data science projects and internships. Currently, I am a Master of Science in Business Analytics student at UCLA Anderson School of Management, with an Anderson Merit Fellowship.
Projects I’ve worked on have focused on data-driven solutions to increase efficiency and accuracy in business problems. I’ve created and maintained structural databases, and built machine learning and artificial intelligence models solving problems about Demand Forecasting, Natural Language Processing, Marketing Analytics and Operations Analytics using numerous tools such as Python, R, AWS and SQL.
During my undergraduate studies, I studied Industrial Engineering and focused on Operations Research, Stochastic Modeling and Applied Mathematics. Then, I completed my Master’s degree in Applied Economics. With the help of my background in engineering and economics, I am able to combine the Data Analytics knowledge with my finance and business skills, to gather more real-life insights from data.
My Data Science internship journey started at Wasserman, where I identified unique and actionable insights related to advertisement targeting in sports marketing. After 3 months of part-time internship at Wasserman, I started interning at Systech Solutions Inc, a data science consulting company. I’ve contributed to two different cases there, which are mainly about demand forecasting and recommendation engines.
During Summer 2019, I worked as a Summer Data Scientist at Boston Consulting Group. I translated data science solutions into business implications for BCG clients.
On the other hand, I am enthusiastic about new technologies. I worked on developing web applications using JavaScript and React tools. Also, I had experience in Blockchain Development, using Solidity, Ethereum’s smart contract development tool.
Data Science, Machine Learning, AI
Python, R, SQL
Data Visualization, Tableau, Power BI
AWS, Microsoft Azure, Databricks, Spark
C, C++, HTML, CSS
Microsoft Excel, Alteryx
Javascript, React, Next.js
Bilingual English/Turkish, Intermediate German/Spanish
Predicted long-term opioid therapy using random forest with 0.92 AUC. Applied extensive feature engineering and developed a personalized recommendation flow to identify and intervene patients at risk for continued long-term use of opioids. Presented the solution pipeline for long term opioid use to Humana Executives and Texas A&M faculty for the final round of the competition.Awarded $30,000 first-place prize over 1,300 masters level students representing over 80 major universities in the United States.
Leveraged Python to build propensity score and meta-learner models to evaluate loyalty program effectiveness. Built an XG-boost algorithm to predict loyalty program membership and estimated individual treatment effects on incremental revenue by t-learner approach.Derived insights from variable importances and Shapley values to provide business recommendations to Choice Hotel on improving the loyalty program.
I am interested in managing and analyzing marketing performance to maximize its effectiveness and optimize return on investment. Understanding marketing analytics allows marketers to be more efficient at their jobs and minimize costs.
I am passionate of the way that Product analytics allows to fully understand how users engage with what companies build. It is especially useful for technology products where teams can track users’ digital footprints to see what leads them to engage, return, or churn.
As a Data Scientist, my responsibilities include cleaning data, making visualizations and deploying predictive models. In addition to these, I am interested in data engineering part as well, which is the aspect of data science that focuses on big data, practical applications of data collection, storage and analysis.
