Roopesh Ranjan
Senior Director, Machine Learning Science at Expedia Group
Bengaluru, Karnataka, India
Summary
🤩
Rockstar🎓
Top SchoolRoopesh Ranjan is a Senior Director of Machine Learning Science based in Bengaluru with nearly two decades of statistical and ML experience and eight years in senior product-facing roles. He leads teams at Expedia Group solving computational advertising and bidding problems, blending research rigor with real-world impact across travel ad platforms. His background spans research scientist roles at Amazon and GE and a PhD in Statistics from the University of Washington, where his probabilistic forecasting work won the Zellner Prize. Roopesh contributes practical ML examples to popular AWS SageMaker notebooks, including a breast cancer prediction demo that underscores his focus on reproducible, deployable solutions. He brings deep expertise in forecasting, risk modeling and applied research, consistently translating academic advances into production systems. Colleagues would describe him as a leader who pairs strong quantitative foundations with a pragmatic drive to operationalize models at scale.
8 years of coding experience
15 years of employment as a software developer
PhD, Statistics, PhD, Statistics at University of WashingtonBachelor and Master of Statistics, Statistics, Bachelor and Master of Statistics, Statistics at Indian Statistical Institute
Stackoverflow
1reputation
0reached
1answer
0questions
Github Skills (13)
jupyter-notebook10
machine-learning10
aws10
data-science10
sagemaker10
python9
data-analysis9
deep-learning8
pandas8
data-preprocessing8
scikit7
scikit-learn7
r6
Programming languages (1)
Github contributions (1)
aws/amazon-sagemaker-examples
Nov 2017 - Jan 2018
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠Amazon SageMaker.
Role in this project:
Data Scientist
Data ScientistContributions:9 commits, 1 PR, 1 branch in 1 month
Contributions summary:Roopesh contributed to example Jupyter notebooks within the `aws/amazon-sagemaker-examples` repository, specifically focusing on breast cancer prediction using a dataset. Their contributions involved data loading, preprocessing, and analysis within a notebook environment, demonstrating the application of machine learning techniques. The user showcased model training and evaluation, with the notebook demonstrating how to build, train, and deploy machine learning models using Amazon SageMaker.
amazon-sagemakerjupyter-notebookmachine-learning-modelssagemakeraws
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