Open to PhD & Full-time Opportunities

Hi, I'm Najmus Sakib

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About Me

A bit about who I am and what I do.

Najmus Sakib

Hey! I'm Najmus — a data analyst and ML practitioner based in Texas. I hold an M.Sc. in Computer Science from Lamar University and a B.Tech. in CSE from NIT Warangal, where I was awarded the ICCR Scholarship by the Government of India.

I've worked across data analytics, machine learning, and software engineering — from analyzing 10M+ clinical records at Script Care Ltd to building developer tools at Amazon Music. My toolkit spans Python, SQL, Scikit-learn, Tensorflow, PyTorch, React, and more. These days I'm diving deeper into AI and exploring what's possible with modern LLMs and generative models.

Outside of work, I'm a passionate Manchester United supporter ☞ yes, even through the rough patches. I unwind by playing video games and getting lost in a good book. I'm currently looking for PhD opportunities in AI/ML and open to exciting full-time roles.

Location

Beaumont, TX, USA

Email

snajmus16@gmail.com

Education

M.Sc. CS, Lamar University

Looking for

PhD & Full-time Roles

Download Resume

Education

My academic journey.

🏆

ICCR Scholarship

Full scholarship awarded by the Government of India for talented international students

🥈

Inter NIT & IIT ICPC — 2nd Place

Competitive programming competition among India's top technical institutes

Master of Science (M.Sc.) in Computer Science

Lamar University

Jan 2022 – Dec 2023

Beaumont, TX, USA

Focused on machine learning, distributed systems, and algorithm analysis.

  • ▸ Relevant Coursework: Software Engineering, Computer Networks, Distributed Computing Systems, Advanced Operating Systems, Analysis of Algorithms

Bachelor of Technology (B.Tech.) in Computer Science and Engineering

National Institute of Technology Warangal

Jun 2017 – Oct 2021

Warangal, India

Graduated from one of India's premier technical institutes. Recipient of the ICCR Scholarship awarded by the Government of India.

  • ▸ Relevant Coursework: Foundations of CS, Linear Algebra, Algorithms, Numerical Methods, Automata & Computability, Artificial Intelligence, OS, DBMS, IoT
  • ▸ 2nd place — Inter NIT & IIT ICPC Coding Competition
B.Tech Thesis

Attentive-Normalized Generation of Images for Semantic Layout Information

  • ▸ Designed a novel GAN architecture for layout-to-image synthesis, introducing Attentive Normalization (AN) for improved semantic consistency
  • ▸ Built a multi-stage generator (Object Composer → Encoder → convLSTM Object Fuser → Decoder) with dual discriminators
  • ▸ Trained on MS COCO (70k+ images) — achieved FID 38.02 vs. 38.14 and 50.7% vs. 49.8% classification accuracy over Layout2Im
  • ▸ Applied to CIFAR-10 class imbalance correction, improving ResNet-110 classification across severely imbalanced classes
Python PyTorch GANs convLSTM MS COCO Computer Vision

Experience

Where I've worked and what I've built.

Data Analyst

Script Care Ltd

Full-time Oct 2024 – Sep 2025 Beaumont, TX

Conducted large-scale statistical research and deployed ML solutions for a leading pharmacy benefits management company.

  • ▸ Analyzed 10M+ pharmacy claims and member records using SQL and Python, contributing to a 15% improvement in rebate accuracy
  • ▸ Built predictive models for claims cost forecasting, enabling early identification of $250K+ in potential cost discrepancies
  • ▸ Developed automated healthcare utilization and cost-containment reports, reducing manual reporting time by 40%
  • ▸ Conducted variance and trend analyses supporting quarterly business reviews, improving operational efficiency by 20%
  • ▸ Ensured 100% audit readiness for state-level PBM submissions through peer-reviewed validation processes
Python SQL Tableau Scikit-learn Excel Pandas

Research Assistant (Data Science)

Lamar University

Research Mar 2024 – Oct 2024 Beaumont, TX

Applied NLP and machine learning to disaster event detection from social media data.

  • ▸ Scraped and compiled 27,000+ disaster-related tweets across 9 major events (2011–2019) using BeautifulSoup and Python
  • ▸ Engineered labeled datasets for supervised ML models in disaster detection and event classification
  • ▸ Applied Logistic Regression, SVM, and LSTM models to classify disaster tweets, flagging events within an average of 4 minutes
Python BeautifulSoup Scikit-learn LSTM NLP Pandas

Software Development Engineer Intern

Amazon Music

Internship May 2023 – Aug 2023 Los Angeles, CA

Worked on the art19 platform, improving developer tooling and deployment infrastructure for a team of 200+ engineers.

  • ▸ Designed and built a dynamic feature flag service using Ember.js, reducing deployment times by 20%
  • ▸ Built a user-centric interface for feature flag management, improving real-time operability and access control for 200+ developers
  • ▸ Established a "source of truth" for feature flags, significantly reducing configuration errors across dev and QA teams
  • ▸ Authored a comprehensive design document detailing requirements, proposed solutions, and test cases
  • ▸ Participated in full Agile cycle: sprint planning, stand-ups, retrospectives; code reviews via Git, task tracking via JIRA
JavaScript Ember.js Git JIRA Agile CI/CD

Data Analyst

Lamar University

Full-time May 2022 – Dec 2023 Beaumont, TX

Built data-driven systems and ML models to support university-wide academic decision-making for 8,000+ students and 520+ faculty.

  • ▸ Developed ML models in Python to predict student success, leading to a 10% increase in student retention
  • ▸ Engineered SEDIE (Strategic Educational Data Insight Engine) using Pandas and Matplotlib, enabling data-driven evaluation for 520 faculty
  • ▸ Built a course recommendation system using Clustering and SVM, optimizing course selection for 8,000+ students
  • ▸ Analyzed enrollment data for 70 courses; presented findings in Excel and Tableau to university leadership
  • ▸ Extracted and manipulated 20,000+ student records using SQL for timely data analysis
Python SQL Pandas Scikit-learn Tableau Matplotlib

Software Engineer

Web and Development Service Centre, NIT Warangal

Part-time Jul 2018 – Oct 2020 Warangal, India

Developed full-stack MIS applications and data dashboards for a university community of 10,000+ users.

  • ▸ Built full-stack solutions using Python and JavaScript for MIS applications used by 10,000+ students and faculty
  • ▸ Designed KPIs and reporting dashboards to track performance metrics and optimize departmental operations
  • ▸ Designed responsive UIs with Bootstrap; implemented backend APIs focused on scalability
Python JavaScript Bootstrap REST APIs SQL

Projects

Things I've built — from solo experiments to team collaborations.

Featured

Attentive-Normalized GAN for Image Synthesis

Novel GAN architecture for layout-to-image synthesis introducing Attentive Normalization (AN). Trained on MS COCO (70k+ images), achieving FID 38.02 vs. 38.14 baseline and 50.7% vs. 49.8% classification accuracy over Layout2Im. Also applied to CIFAR-10 class imbalance correction.

Python PyTorch GANs CNNs MS COCO convLSTM

SoccerIQ — Agentic Match Insights

AI-powered football analytics platform using LangChain and GPT-4o to answer natural-language queries about match data. Analyzes 38-match seasons across MLS and European leagues, generating 10+ derived KPIs. Reduced manual analysis time by ~70%.

Python LangChain GPT-4o Streamlit API-Football

Fraud Risk Prediction — Credit Card Transactions

End-to-end fraud detection pipeline on the ULB dataset (0.17% fraud rate). Applied SMOTE for class balancing and tuned a Random Forest classifier achieving 98.3% ROC-AUC and 89% recall — identifying the most predictive PCA components (V17, V14, V12).

Python Scikit-learn SMOTE Random Forest PCA Pandas

Other Projects

Texas Election Shift Analyzer

Interactive dashboard analyzing county-level voting pattern shifts across Texas (2016–2024). Features choropleth maps, stacked bar charts, and AI-generated political insights via GPT-4.

Python Streamlit Plotly GPT-4 Pandas GeoJSON

Pharmacy Claims Fraud Detection

End-to-end healthcare fraud detection system for pharmacy billing data. Uses Azure SQL for data warehousing with Python-based ML models to flag anomalous claims patterns.

Python Azure SQL Machine Learning SQL

Stroke Risk Prediction

Predictive model assessing stroke risk from patient health data (age, gender, health conditions, smoking status), achieving 95% accuracy. Comprehensive EDA with Matplotlib and Seaborn.

Python Scikit-learn Pandas NumPy Matplotlib Seaborn

Strategic Educational Data Insight Engine (SEDIE)

Python analytics platform processing 20,000+ student records to support data-driven decision-making for 520+ faculty. Features comparative studies, interactive visualizations, and a course recommendation system using Clustering and SVM.

Python Pandas Matplotlib Scikit-learn SQL SVM

Multi-Threaded Chat Room Service

Client-server application in C enabling real-time multi-client communication via socket connections and pthreads. Implements server-side queue management, thread handling, and built-in error recovery.

C Sockets pthreads Systems Programming
View all on GitHub

Tech Stack

Technologies I work with and love.

💻

Languages

Python JavaScript TypeScript C / C++ SQL Java
🤖

Machine Learning & AI

PyTorch TensorFlow Scikit-learn GANs CNNs NLP LangChain Computer Vision
📊

Data Science

Pandas NumPy Tableau Matplotlib Seaborn Excel
🌐

Web Technologies

React.js Node.js Django Flask Ember.js REST APIs Bootstrap HTML / CSS
🗄️

Databases

MySQL Oracle DB AWS DynamoDB Azure SQL NoSQL
🛠️

Tools & Platforms

Git / GitHub AWS Docker CI/CD Linux Agile / JIRA Streamlit

Books I've Read

A curated reading list that shaped how I think about software.

📖
Software Engineering

The Pragmatic Programmer

David Thomas, Andrew Hunt

★★★★★

"Essential reading for any developer. Timeless advice on writing better code and building a career."

📖
Software Engineering

Clean Code

Robert C. Martin

★★★★★

"Transformed how I think about naming, functions, and code organization."

📖
Systems

Designing Data-Intensive Applications

Martin Kleppmann

★★★★★

"The best book on distributed systems fundamentals. Brilliant depth."

📖
Machine Learning

Deep Learning

Ian Goodfellow, Yoshua Bengio, Aaron Courville

★★★★☆

"The foundational ML textbook. Dense but comprehensive."

📖
Algorithms

The Algorithm Design Manual

Steven Skiena

★★★★★

"Best practical algorithms book I've read. War stories section is gold."

Get In Touch

Have an opportunity, question, or just want to say hi? My inbox is open.

Whether you're a professor with a PhD opening, a recruiter with an exciting role, or a fellow researcher — I'd love to hear from you. I typically respond within 24 hours.

GitHub

github.com/Najmussakib93

LinkedIn

www.linkedin.com/in/najmussakib97/

Email

snajmus16@gmail.com

Send a Message