Ram Seshadri
Data Scientist
New York, New York, United States
Summary
👤
SeniorRam Seshadri is a Data Scientist and Staff AI Consultant at Google with nine years of experience helping Fortune 50 companies execute agentic AI transformations. Based in New Jersey, he blends hands-on engineering with strategic advisory work, shipping practical ML solutions and improving data-driven decision processes. An active open-source contributor, he created and substantially enhanced AutoViz—streamlining dataset visualization and adding novel feature-selection and dimension-reduction fixes that improve scalability and interpretability. Ram also teaches and consults, translating complex model behavior into actionable guidance for product and executive teams. He is known for turning visualization and feature engineering challenges into reliable tooling that accelerates enterprise AI adoption.
9 years of coding experience
French
Github Skills (14)
scikit10
data-visualizations10
data-analysis10
pandas10
xgboost10
machine-learning10
data-visualization10
data-visualisation10
python10
feature-selection10
scikit-learn10
automl9
automated-machine-learning9
heatmap9
Programming languages (3)
Github contributions (5)
AutoViML/AutoViz
Nov 2019 - Dec 2021
Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
Role in this project:
Data Scientist
Data ScientistContributions:12 commits, 4 comments in 2 years
Contributions summary:Ram contributed to the `autoviz` repository by fixing bugs, updating code, and improving the visualization capabilities. Specifically, they addressed issues related to the dimension reduction using XGBoost, updated the heatmap, barplots and fixed a bug in classify columns. The user also implemented a new uncorrelated algorithm for feature selection. Overall, the user focused on improving the core functionality and performance of the data visualization features within the project.
xgboostpythonvisualizeholoviewsdata-science
rsesha/deep_autoviml_pipeline
Dec 2021 - Feb 2022
Quick tutorial on orchest.io that shows how to build multiple deep learning models on your data with a single line of code using the popular python library, Deep AutoViML.
Contributions:4 commits, 2 PRs, 7 pushes in 1 month
deep-learningpython
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