Atul Bhardwaj

Atul Bhardwaj

Data Science Graduate · ML & Cloud Engineer · Solutions Architect · Building Scalable AI Solutions

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About Me

Driven by data. Focused on impact.

I'm Atul, a Data Scientist and Cloud Engineer finishing my MS in Computer Science at Seattle University (GPA 3.6, graduating Jun 2026). I specialize in turning large-scale data into systems that work in production.

I've built a serverless AWS data lake that cut query costs by 80%, currently building a RAG chatbot on Bedrock and Lambda to help students access campus services faster, and also advise cross-functional teams as a Data Science Consultant at Statistics Without Borders. I hold 3 AWS certifications and have 2 peer-reviewed publications in ML and NLP.

I'm looking for Data Science, ML Engineering, or Cloud Engineering roles where I can keep building things that matter.

3x
AWS Certified
3.6+
GPA
6+
Projects Shipped
2
Publications

Education

Academic foundation in data science and computer science

Masters in Computer Science — Data Science Specialization
Seattle University
GPA 3.6  ·  Expected Jun 2026  ·  Seattle, WA
Courses: Artificial Intelligence, Machine Learning, Data Science, Big Data Analytics, Visual Analytics, LLM
Bachelor of Technology — Computer Science Engineering
Maharshi Dayanand University
GPA 3.5  ·  Sep 2020 – May 2024  ·  Haryana, India
Activities: Data Science Club Captain — led 100+ members, organized webinars, guest speaker sessions, and coding competitions

Professional Experience

Building impact through data-driven solutions

Data Science Consultant
Statistics Without Borders
Sep 2025 – Present · Seattle, WA
  • Advised on data strategy for Women in Sport initiative (14+ datasets, n > 2,200) — technical guidance, QA, and management recommendations.
  • Turned complex statistical results into executive-ready visuals for non-technical stakeholders.
  • Mentored 7 project teams on reproducible workflows and data storytelling.
Operations Technician & Customer Service Specialist
Seattle University
Jun 2025 – Present · Seattle, WA
  • Building an AWS RAG chatbot (Bedrock, Lambda, S3) for campus services — expected to cut response time from hours to seconds.
  • Analyzed parking demand with Python and clustering; built Power BI dashboards presented to Director for operational improvements.
  • Serve customers, train front desk staff, and collaborate with IT on networking issues.

Featured Projects

Solving real-world problems with data and code

AI / ML
Amazon Lex Banking Chatbot

Amazon Lex Banking Chatbot

November 2025

Conversational AI chatbot for banking transactions with custom intents, slot types, and multi-turn context management.

  • AWS Lambda integration for dynamic responses
  • Infrastructure as code via CloudFormation
Amazon Lex AWS Lambda CloudFormation NLU
NeatCode - AI Code Refactoring

NeatCode — AI-Powered Code Refactoring

In Progress

Full-stack tool providing intelligent code refactoring suggestions using Google Gemini AI.

  • Python Flask backend with RESTful file upload API
  • Team of 3 using Agile (4 sprints)
Python Flask Gemini AI REST API
GitHub → Live Demo →
Data Engineering & Cloud
YouTube Trending Analytics AWS Data Lake

YouTube Trending Analytics — AWS Data Lake

November 2025

Serverless pipeline analyzing 1,000+ trending videos across 5 regions with automated ETL workflows.

  • Three-tier S3 lake: Raw → Cleansed → Analytics
  • 75% storage reduction · 80% query cost savings
  • Athena SQL + QuickSight dashboards
AWS PySpark Glue Athena Parquet
GitHub →
Real-Time TF-IDF Search Engine

Real-Time TF-IDF Search Engine

March 2025

Scalable distributed search engine on AWS infrastructure with real-time ranking over large-scale text datasets.

  • Apache Spark on EMR for parallel processing
  • TF-IDF algorithm with EC2 · S3 · DynamoDB · Lambda
Apache Spark AWS EMR DynamoDB Python
Data Science & Analytics
Campus Operations Analytics

Campus Operations — Behavioral Segmentation

September 2025

Unsupervised learning pipeline to segment departmental behavior patterns and optimize campus operations.

  • 5 distinct segments via GMM + K-Means clustering
  • 25% reduction in unnecessary support tickets
Python K-Means GMM Scikit-learn

Technical Skills

Tools and technologies I work with

Languages
Python SQL PySpark Scala R Bash JavaScript
ML & AI
Scikit-learn TensorFlow PyTorch SageMaker Bedrock RAG Time-Series Clustering
AWS & Cloud
Lambda S3 EC2 Glue Athena EMR Redshift Lex CloudFormation Azure ML
Data & Visualization
Tableau Power BI ETL Pipelines Data Modeling Matplotlib Plotly
Databases & Big Data
PostgreSQL MySQL DynamoDB CosmosDB Apache Spark Hadoop Hive
Tools & Practices
Git Docker Linux/Unix Jira Agile Model Validation

Research Publications

Peer-reviewed contributions to the field

Evaluating ML Algorithms for Heart Disease Prediction

IEEE Xplore 2024 Read Paper →

Comprehensive evaluation of six supervised ML algorithms for heart disease prediction, optimizing precision, recall, and F1-score to minimize false negatives in clinical decision support systems.

Bidirectional LSTM for Toxic Comment Classification

EAI Journal 2024 Read Paper →

Novel Bidirectional LSTM + Convolution approach for identifying and classifying toxic comments online, demonstrating improved NLP performance across benchmark datasets.

AWS Certifications & Credentials

Verified cloud and data science credentials

View all badges on Credly ↗
Amazon Web Services
↗ AWS Solutions Architect Associate

Solutions Architect – Associate

Amazon Web Services February 2026
↗ AWS Cloud Practitioner

Certified Cloud Practitioner

Amazon Web Services February 2026
↗ AWS AI Practitioner

AI Practitioner

Amazon Web Services February 2026
AWS ML Engineer Associate

ML Engineer – Associate

Amazon Web Services Actively Preparing

Get In Touch

Let's connect and create something impactful together

📧
Email mail@atulbhardwaj.com
📍
Location Seattle, Washington, USA
💼
LinkedIn linkedin.com/in/atul1000
💻
GitHub github.com/4uatul