Kapil Varshney
Data Scientist
Delhi, India
Summary
🤩
Rockstar🎓
Top SchoolKapil Varshney is a Data Scientist based in Delhi with 10 years of experience applying computer vision and deep learning to real-world problems, particularly in aerial and inspection imagery. At Esri he integrates state-of-the-art models into the ArcGIS Python API, publishes pretrained models to the Living Atlas, and authors sample notebooks and documentation to help users deploy solutions like object detection, segmentation, OCR and vehicle tracking. His background blends hands-on model development (Keras/TensorFlow, OpenCV) with product-focused research, testing and API enhancements that improve model performance and usability. Earlier roles as an instructor, founder and aerospace design lead reflect his ability to communicate complex concepts, build teams, and drive projects from prototype to profitable operations. He contributed a vehicle detection and tracking notebook to the widely used Esri arcgis-python-api repo, demonstrating end-to-end workflows from training to inference and tracking. Practical, cross-domain experience—ranging from aerospace digitization to startup growth—gives him a rare mix of engineering rigor and operational impact.
10 years of coding experience
3 years of employment as a software developer
Data Science, Data Science at Byte AcademyAerospace Engineering, Aerospace Engineering at Florida Institute of TechnologyIndian School Certificate, Indian School Certificate at City Montessori School, Gomti Nagar, Lucknow, IndiaGEA Summer Aerospace Program, Toulouse, FranceUniversity of Illinois Urbana-Champaign
English, Hindi
Github Skills (7)
object-detection10
computer-vision10
jupyter-notebook10
deep-learning10
python10
data-science9
machine-learning9
Programming languages (2)
Github contributions (5)
Esri/arcgis-python-api
Dec 2019 - Jan 2023
Documentation and samples for ArcGIS API for Python
Role in this project:
Data Scientist
Data ScientistContributions:20 reviews, 56 commits, 24 PRs in 3 years 1 month
Contributions summary:Kapil added a sample notebook demonstrating vehicle detection and tracking using deep learning. This included importing necessary libraries, loading and preparing training data, visualizing it, loading a model architecture, training the model, previewing results, and ultimately, saving the model. The notebook also covered the inference and tracking process, providing a comprehensive overview of the vehicle detection and tracking workflow.
apiarcgispythonjupyterlab-extensiondata-science
kapil-varshney/utilities
Aug 2018 - Mar 2019
Contributions:11 commits, 8 pushes, 1 comment in 7 months
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