Atul Bhardwaj

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.

AWS Certified

3.6+

GPA

Projects Shipped

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

NeatCode — AI-Powered Code Refactoring

March 2026

Full-stack app deployed to AWS — paste messy code, get it refactored by Google Gemini AI with explanations for every change so you're learning, not just copying.

Flask backend on Elastic Beanstalk (Dockerized) for managed deployments and health checks
React frontend hosted on S3 with CloudFront CDN for edge caching
Input guard rails — rejects non-code input and prompts the user to try again
Started as an academic project (team of 3, Agile/4 sprints), extended and deployed solo

Python Flask React Gemini AI AWS Elastic Beanstalk S3 CloudFront Docker

GitHub → Live Demo →

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

Data Engineering & Cloud

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

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 — 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.