CV

Work experience

• Google, Senior Research Data Scientist (06/2022 - Present)
  • I work on model evaluations for post training and finetuning; conduct research and implement algorithms to shape how LLM-as-a-judge models are deployed and used in conjunction with human feedback.
  • Previously, I worked in a central team developing end to end machine learning, statistical modeling and causal inference solutions for multiple products and functions across Google. My work involved implementing custom models from scratch in python and R, 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 from news media, advertising, 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)
  • I conducted analysis to inform decisions and coordinate responses to client needs.

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