CV

Work experience

• Roblox, Senior Machine Learning Engineer (08/2025 - Present)
  • I work on Generative AI models for Roblox Creators.
• Google, Senior Research Data Scientist (06/2022 - 08/2025)
  • I worked on model evaluations; conducted research and implemented algorithms to shape how LLM-as-a-judge models are deployed and used in conjunction with human feedback.
  • Prior to Search, I worked in a central team developing end to end machine learning, statistical modeling and causal inference solutions for multiple products and functions. My work involved implementing custom models from scratch, maintaining and improving a suite of packages and tools that served other teams, and providing expert guidance, custom analyses and consultation.
• Uber Technologies, Applied Scientist II (09/2021 - 06/2022)
  • I worked on the central experimentation platform, maintaining and improving Uber’s experimentation engine where hundreds of experiments are logged and analyzed daily.
  • My work focused on building new features in python and pyspark, implementing scalable solutions to enable efficient experiments using variance reduction techniques from recent research in statistics.
• Facebook, Research Scientist Intern (06/2020-09/2020)
  • I designed and executed a research project for an ads-related product launch on Facebook. I built and maintained data pipelines, conducted analysis from scratch via custom models written in python, and presented results to inform decision making.
• Stanford Graduate School of Business (09/2017-06/2020, 09/2020-09/2021)
  • Researcher
    • I conducted research, building machine learning & AI models to answer policy-relevant questions on a wide variety of domains such as news content classification, valuation predictions in online ad auctions, traffic prediction and congestion pricing. Here are some highlights from my publications:
      • Implementing an end to end dynamic topic model and topic influence model on 10 years of newspaper data to predict award winning articles (published in PNAS).
      • Implementing Bayesian machine learning models to predict advertiser valuations using auction data from Twitter (published in ACM).
      • Implementing traffic prediction models based on GPS data to understand the impact of alternative congestion pricing schemes (Under Review).
  • Research & Teaching Assistant
    • Multiple teaching and research projects under the supervision of: Professors Shoshana Vasserman, Greg Martin, Steven Callander, Takuo Sugaya, Avidit Acharya, Dana Foarta, Peter Reiss, Susan Athey, Niall Keleher.
  • Ph.D. Affiliate, Golub Capital Social Impact Lab
• J.P. Morgan & Chase London, Business Analyst (09/2016-09/2017)

Education

• Stanford University, Ph.D., Graduate School of Business, 2021
• Stanford University, M.S., Statistics, 2020
• University of Cambridge, M.Phil., Economics (Research), 2016
• Bogazici University, B.A., Economics (High Honors), 2015

Skills

• Highlighted Ph.D. and M.S Coursework:
  • Machine Learning, Applied Statistics, Bayesian Statistics, Stochastic Processes, Design and Analysis of Algorithms, Statistical Learning, Experiment Design, Causal Inference, Econometrics, Microeconomics.
• Python, R, Spark, Julia, SQL, Unix, Tensorflow, Keras, PyTorch
• Languages: English, Turkish, French, Japanese