Divya Agarwal
Analyst
C++
SQL
C
Node JS
Python
Active 5 hours ago
+91 (8708) 781557
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Overview
Technical skills
Timeline
Roles
Overview
Frontend UI engineer (junior) focusing on classic DOM-driven interfaces and small fullstack exercises with Express. The strongest proven skill is building interactive snippet-driven pages and simple client-server flows as shown in module5_solution/js/script.js (AJAX loading, template insertion, and UI formatting). The public code lacks component-based React work, test coverage, advanced async state handling, and measured performance or security hardening.
Technical skills
C++
SQL
C
Node JS• Junior
Python
Node JS
Axios
Express
AI/ML
LLM
NLP
Prompt Engineering
OpenCV
Pandas
NumPy
Jupyter Notebook
ChatGPT
Claude
NLTK
Scikit-learn
Sentiment Analysis
Streamlit
Frontend
React.js
Vite
Tailwind CSS
ESLint
PostCSS
autoprefixer
DevOps
Azure
GCP
GitHub
Analytics
ETL/ELT
Tableau
Power BI
Timeline
Analyst
•
Middle
Bain & Company
•
Full-Time
Built an Excel-based automation tool that consolidated SimilarWeb and SEMrush metrics, significantly reducing a manual multi-person research workflow. Developed a Python and Streamlit app to generate marketing diagnostic PowerPoint decks and enabled faster on-demand updates. Integrated ChatGPT and Claude into analytics workflows to speed up insight structuring and report drafting. Fixed a Power BI visualization issue by recreating the chart with Deneb-style custom visuals and delivered client-ready executive presentations using standard consulting tools.
Python
Power BI
Streamlit
ChatGPT
Claude
Junior AI/ML Engineer
Confidence: Medium Generalist
Computer vision practitioner at a junior level focused on video processing and frame-level annotation workflows. The strongest proven skill is practical OpenCV-based video handling and annotation demonstrated in working_with_video_in_python.ipynb, including frame extraction, drawing boxes and labels, and ffmpeg integration for conversion and compression. There is no evidence of model training, evaluation pipelines, experiment tracking, or production-grade MLOps in public code.
Model Architecture & Training
How well models are designed and trained
Not evidenced in public code
Data Pipeline & Feature Engineering
2/10
How data is prepared for models
Basic frame-level data extraction and label alignment using pandas and simple CSV-based transforms.
Evidence
divy-1909/Driving-Video-with-Object-Tracking/working_with_video_in_python.ipynb: reading /content/mot_labels.csv.zip and filtering videoName
divy-1909/Driving-Video-with-Object-Tracking/working_with_video_in_python.ipynb: video_labels['video_frame'] = (video_labels['frameIndex'] * 11.9).round().astype('int')
Experimentation & Evaluation
How results are measured and tested
Not evidenced in public code
MLOps & Deployment
How models are shipped to production
Not evidenced in public code
Computational Efficiency
1/10
How efficiently computing resources are used
Minimal efficiency awareness through use of cv2.VideoWriter for streaming frames and tqdm plus ffmpeg for final compression.
Evidence
divy-1909/Driving-Video-with-Object-Tracking/working_with_video_in_python.ipynb: loop writing frames with tqdm and cv2.VideoWriter('out_test.mp4', ...)
divy-1909/Driving-Video-with-Object-Tracking/working_with_video_in_python.ipynb: subprocess.run ffmpeg to transcode/compress out_test.mp4 to out_test_compressed.mp4
Research Depth & Innovation
Depth of research and new ideas
Not evidenced in public code
Expertise
Autonomous Vehicles & Self-Driving• Junior
Industries
Artificial Intelligence• Junior
Transportation & Logistics• Junior
Technologies
Python
SQL
C++
Claude
GCP
ChatGPT
OpenCV
Jupyter Notebook
Scikit-learn
Prompt Engineering
NLP
Azure
Pandas
NumPy
LLM
NLTK
Streamlit
Sentiment Analysis
GitHub
GitHub• mentioned only
Recommendations
- Develop video annotation and preprocessing pipelines for computer vision datasets, including robust frame-to-label alignment and batch export utilities.
- Prototype end-to-end demos that combine lightweight object detectors with the existing annotation pipeline for real-time or near-real-time visualization.
- Build tooling around label validation and automated visualization to speed dataset QA and error analysis.
- Integrate simple experiment tracking and evaluation scripts when models are introduced to document metrics and reproducibility.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Junior Frontend Developer
Confidence: Medium UI Engineer
Frontend UI engineer (junior) focusing on classic DOM-driven interfaces and small fullstack exercises with Express. The strongest proven skill is building interactive snippet-driven pages and simple client-server flows as shown in module5_solution/js/script.js (AJAX loading, template insertion, and UI formatting). The public code lacks component-based React work, test coverage, advanced async state handling, and measured performance or security hardening.
UI Component Architecture
2/10
How interface parts are built
Mostly procedural DOM manipulation and template substitution; no component-based or composable UI architecture.
Responsive & Cross-browser
3/10
Works on all screens and browsers
Uses Bootstrap responsive classes and a simple runtime screen-width check to hide navbar on blur; relies on vendor CSS for cross-browser coverage.
Performance Optimization
1/10
Speed of the interface
No measured performance work or advanced optimizations; only basic loading indicator handling is implemented.
Accessibility & Semantics
2/10
Usable for everyone
Accessibility mostly inherited from Bootstrap; minimal custom a11y or focus management beyond a navbar blur handler.
State Management & Data Flow
2/10
Managing data in the app
Simple server-client data flows using AJAX and an Express endpoint; no request cancellation, optimistic updates, caching strategy, or state machines.
UX & Visual Polish
3/10
Look and feel quality
Reasonable basic UX touches such as loading spinner and price/portion formatting, but no deeper UX patterns like skeletons, undo, or nuanced empty/stale states.
Expertise
React• Junior
Modern Web Frameworks• Junior
HTML & CSS• Junior
Frontend Architecture & Build Tools• Junior
Technologies
Node JS• Junior
Tailwind CSS
Express
React.js
Vite
Axios
PostCSS
ESLint
autoprefixer
Recommendations
- Convert the procedural DOM flows in script.js into small reusable React components and implement the corresponding src/main.jsx and component files so you can demonstrate component architecture and state flow.
- Add request cancellation and race handling for AJAX flows and introduce basic client-side cache or server-state library (e.g., Axios abort controllers or React Query) to show async discipline.
- Introduce automated tests (unit tests for server/index.js and simple component tests for UI) and add a CI lint/test job to demonstrate quality and repeatable shipping.
- Replace copied vendor code usage with clear attribution and remove unnecessary vendored files from the main tree; keep only what is required to show original work.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Intern Backend Developer
Confidence: Low Data Platform
A junior developer focused on SQL-driven data analysis and exploratory analytics. The strongest proven skill is SQL data analysis, evidenced by a Music Store SQL Analysis README that lists joins, aggregations and window function usage. There is no public human-authored backend code, production migrations, API implementations, or test suites to evaluate system design, scalability, or security practices.
API Design
How well APIs are designed
Not evidenced in public code
Data Layer & Database
Working with databases
Not evidenced in public code
Scalability & Performance
Handling load and speed
Not evidenced in public code
System Architecture
Overall system structure
Not evidenced in public code
Security & Auth
Protecting data and access
Not evidenced in public code
Reliability & Observability
Stability and monitoring
Not evidenced in public code
Industries
Data & Analytics• Intern
Recommendations
- Develop SQL-based analytical pipelines and productionize simple ETL jobs that include schema migrations and versioning.
- Build small REST endpoints that expose analytics results with input validation, pagination and error contracts to gain API design experience.
- Add a migration history and simple transactional tests around data updates to demonstrate schema evolution and data integrity handling.
- Implement basic observability: structured logs with request IDs and a few metrics, plus graceful shutdown and timeout handling for services.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: