Sriram Kothim
Data Analytics Manager
Active 3 days ago
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Overview
Technical skills
Timeline
Roles
Overview
A junior frontend developer focused on small interactive web pages and client-side form flows with solid basic DOM and browser API usage. The strongest proven skill is building a working client-side quiz workflow that persists quizzes to localStorage and renders question navigation, as shown in UM QUIZ/Teacher.html and UM QUIZ/quiz.js. The codebase lacks evidence of component architecture, automated tests, server integration, accessibility audits, or measurable performance work.
Technical skills
AI/ML
Pandas
NumPy
Jupyter Notebook
Frontend
Frontend
Analytics
Matplotlib
Timeline
IT Analyst
•
Middle
Techdoquest
•
Full-Time
I gathered requirements from US clients like KKR and Tech Mahindra to recruit candidates
Junior Frontend Developer
Confidence: Medium App Engineer
A junior frontend developer focused on small interactive web pages and client-side form flows with solid basic DOM and browser API usage. The strongest proven skill is building a working client-side quiz workflow that persists quizzes to localStorage and renders question navigation, as shown in UM QUIZ/Teacher.html and UM QUIZ/quiz.js. The codebase lacks evidence of component architecture, automated tests, server integration, accessibility audits, or measurable performance work.
UI Component Architecture
2/10
How interface parts are built
Small-scale DOM-driven UI built with imperative functions and direct element creation; no component system, composition patterns, or reusable UI primitives are present.
Evidence
UM QUIZ/quiz.js: loadQuestion, saveAnswer and calculateScore functions that manipulate DOM directly
UM QUIZ/Teacher.html: dynamic questionDiv generation inside a single script block rather than reusable components
Responsive & Cross-browser
2/10
Works on all screens and browsers
Basic responsive adjustments via a single media query and proper viewport meta tag, but no advanced layout techniques, RTL or feature detection.
Evidence
UM QUIZ/quiz.css: @media (max-width: 768px) rule adjusting container width
UM QUIZ/quiz.html: <meta name="viewport" content="width=device-width, initial-scale=1.0">
Performance Optimization
1/10
Speed of the interface
No measurable or explicit performance work; synchronous DOM updates and full re-renders per question with no code-splitting or virtualization.
Evidence
UM QUIZ/quiz.js: single-threaded DOM updates when loading each question and building answerOptions via innerHTML
UM QUIZ/Teacher.html: synchronous form creation on submit and storing full quizData to localStorage without batching
Accessibility & Semantics
3/10
Usable for everyone
Some semantic elements and label usage exist and answers are wrapped in label elements, but there is no ARIA, focus management, keyboard hints, or accessibility testing.
Evidence
UM QUIZ/quiz.js: answer option markup uses <label> wrapping <input> which preserves basic click/label semantics
UM QUIZ/Teacher.html: uses <label> elements tied to inputs for dynamically generated questions
State Management & Data Flow
2/10
Managing data in the app
Very simple client-side state using localStorage and in-memory arrays; no server-state discipline, optimistic updates, caching strategy, request cancellation or state machines.
Evidence
UM QUIZ/quiz.js: JSON.parse(localStorage.getItem('quizData')) and local state array userAnswers
UM QUIZ/Teacher.html: builds quizData and stores via localStorage.setItem('quizData', JSON.stringify(quizData))
UX & Visual Polish
3/10
Look and feel quality
Reasonable basic UX for a small quiz app - navigation, next/previous, hover styles and a score display - but no loading/error/empty skeletons or undo and limited edge-state handling.
Evidence
UM QUIZ/quiz.js: next/prev button handlers and score displayed in #score element
UM QUIZ/quiz.css: hover styles and styled .question and .answer-options for visual polish
Expertise
HTML & CSS• Junior
PWA & Web APIs• Junior
Industries
Education• Junior
Technologies
Frontend
Recommendations
- Assign to build small interactive educational tools or prototypes that require DOM manipulation, form flows and client-side persistence.
- Have them implement feature work that extends the current quiz - e.g., add form validation, persistent user sessions (server-backed), and basic end-to-end tests.
- Pair with a senior engineer to refactor the UI into reusable components and introduce accessibility improvements and performance measurement instrumentation.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Intern AI/ML Engineer
Confidence: Low Generalist
An entry-level data analyst focused on exploratory analysis and visualization using notebook workflows and core Python data libraries. The strongest proven skill is basic data wrangling and plotting with Pandas and NumPy as evidenced by a Jupyter notebook that generates, cleans, aggregates, and visualizes synthetic movie and weather datasets. There is little to no evidence of production-grade engineering, experiment tracking, model training, deployment, or nontrivial data pipeline design in the publicly available code.
Model Architecture & Training
How well models are designed and trained
Not evidenced in public code
Data Pipeline & Feature Engineering
How data is prepared for models
Not evidenced in public code
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
How efficiently computing resources are used
Not evidenced in public code
Research Depth & Innovation
Depth of research and new ideas
Not evidenced in public code
Verified artifacts
Recommendations
- Develop small end-to-end EDA and reporting projects that include data provenance and reproducible notebook-to-script conversion so results can be productionized.
- Practice structured data-cleaning pipelines and write unit tests for key transformation functions to demonstrate engineering rigor beyond exploratory notebooks.
- Build simple visualization dashboards or static reports (e.g., Streamlit or exported scripts) to show deliverable-focused work for stakeholders.
- Add minimal experiment tracking or metadata logging (MLflow or simple CSV logs) when running analysis to demonstrate reproducibility.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Intern Data Scientist
Confidence: Low Generalist
Early-career data analyst at Intern level who focuses on small-scale exploratory analyses using synthetic datasets. The strongest proven skill is generating and visualizing synthetic data with Pandas, NumPy and Matplotlib in a Jupyter notebook that builds and analyzes a 100-movie synthetic dataset. There is no evidence of production-grade pipelines, automated testing, environment pinning, or large-scale data engineering in public code.
Statistical Rigor
Correct use of statistics
Not evidenced in public code
Data Wrangling & Cleaning
Preparing and cleaning data
Not evidenced in public code
Exploratory Analysis & Visualization
Exploring and visualizing data
Not evidenced in public code
Predictive Modeling
Building models that predict
Not evidenced in public code
Business Insight & Impact
Turning analysis into business value
Not evidenced in public code
Reproducibility & Notebook Hygiene
Clean, repeatable analysis
Not evidenced in public code
Technologies
Jupyter Notebook
Matplotlib
Pandas
NumPy
Concepts• mentioned only
Python• mentioned only
Recommendations
- Develop instructional EDA notebooks and short demo dashboards that showcase data cleaning, aggregation and visualization using Pandas and Matplotlib.
- Convert notebook analysis into small reproducible pipelines by adding a requirements.txt or environment.yml, modular scripts, and explicit random seeds.
- Add simple unit tests or data validation checks and document assumptions and methodology inside notebooks to improve reproducibility and rigor.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: