Suhail

Building intelligent
systems from data.

Data Scientist Applied ML Engineer

I build data-driven and machine learning solutions that turn complex data into measurable impact, with a focus on healthcare AI.

Python
SQL
Machine Learning
AWS
Healthcare AI

View Projects View Resume

Tools & Technologies

Python
SQL
pandas
NumPy
scikit-learn
PostgreSQL
AWS

01 / About

Data science, built
for real systems.

Data Scientist with 4+ years of experience across data analytics, healthcare, and technology-driven environments, specializing in Python, SQL, predictive modeling, and applied machine learning.

My work spans feature engineering, model evaluation, data quality, and reproducible ML workflows, supported by hands-on experience with PostgreSQL, REST APIs, AWS, and production systems. I focus on building analytical and machine learning solutions designed for real-world integration and deployment.

Education

Master of Science in Information Technology

St. Francis College

Brooklyn, NYGPA 3.85 / 4.00Aug 2025

Bachelor of Engineering in Computer Science & Engineering

Osmania University

Hyderabad, IndiaGPA 3.67 / 4.00Jul 2023

02 / Experience

Work at the intersection of data, systems, and care.

NYRX at Ralph LLC

New York, NY

Data Scientist

Mar 2026 — PresentCurrent

Developed a medication demand forecasting solution from pharmacy billing and ordering history, using time-based features and predictive models to estimate future demand and surface potential inventory mismatches.
Established the ML workflow from data preparation through model evaluation and deployment, incorporating experiment tracking, model versioning, containerized inference, and performance monitoring for repeatable model releases.
Designed PostgreSQL data models and validation workflows for healthcare and pharmacy data, improving the reliability of data used across analytics, operational reporting, and machine-learning workflows.
Built and maintained REST APIs and backend services with Node.js, Express.js, PostgreSQL, Redis, and AWS, providing the production infrastructure needed to connect application workflows with analytical and ML components.
Led backend and data implementation within a five-person engineering team, working directly with company leadership and coordinating with frontend, DevOps, and QA through development, testing, and deployment.

Junior Data Analyst

Oct 2025 — Mar 2026

Analyzed pharmacy billing, inventory, and operational data using SQL, Excel, and Power BI, cleaning and reconciling records to identify quantity discrepancies, usage patterns, and inventory trends.
Built reporting workflows and dashboards to compare medication billing and ordering activity, giving management clearer visibility into shortages, excess quantities, and purchasing needs.

Hands Industries FZC

Sharjah, UAE (Remote)

Data Analyst I

Aug 2020 — Oct 2023

Analyzed customer, sales, and operational datasets using SQL, Excel, Tableau, and Power BI, cleaning and organizing approximately 5,000–10,000 records to identify customer trends, recurring issues, and business performance patterns.
Built dashboards and structured reporting workflows from manually maintained business records, helping management track customer and operational trends and use data more effectively for decision-making.

03 / Leadership

Leadership beyond the role.

Organization leadership

President

ACE Lords — Department of Computer Science & Engineering

L.I.E.T

2021 — 2023

Led a 15-member technical organization serving a 300–400 student CSE department; organized 10 technical workshops, a datathon, and a three-day technical event featuring 8 competitions and 800+ participants. Received a Letter of Appreciation for two years of leadership.

04 / Selected work

From complex data
to intelligent systems.

Featured project / 01

DATAPatient Records

MODELRisk Prediction

INSIGHTClinical Decision

Completed retrospective study

30-Day Hospital Readmission Risk Prediction

End-to-end machine learning pipeline for identifying hospital encounters involving patients with diabetes at elevated risk of readmission within 30 days.

Read case study View workflow

Pharmacy demand history, forecast curve, and production workflow

Production ML system

Pharmacy Demand Forecasting & MLOps Pipeline - NYRX

Engineered temporal, lag, rolling, and calendar features from pharmacy billing and ordering history and compared forecasting approaches using time-aware validation and baseline-relative evaluation.
Built a reproducible ML lifecycle covering experiment tracking, model versioning, automated testing, containerized inference, prediction logging, and monitoring for integration with existing application workflows.

View project details View code on GitHub

01 / 02

05 / Technology Stack

From modeling
to production.

A connected stack of technologies and practices I use across the machine learning lifecycle.

Built for real-world systems

End-to-end coverageFrom data to deployment

Production readyScalable, reliable, and monitored

Continuous improvementTrack, evaluate, and iterate

Programming

Python
SQL
JavaScript

Machine Learning & Data Science

pandas
NumPy
scikit-learn
XGBoost
SciPy
statsmodels
SHAP

Feature Engineering
Predictive Modeling
Time-Series Forecasting
Model Evaluation

MLOps & ML Engineering

MLflow
FastAPI
Docker
GitHub Actions

Experiment Tracking
Model Versioning
Model Serving
Monitoring

Data & Visualization

PostgreSQL
Redis
Excel
Power BI
Tableau
Matplotlib

Cloud & Production

AWS
REST APIs
Node.js
Express.js
Nginx
Linux
Git / GitHub

The stack in action

Data SourcesCollect & Ingest

→

PrepareClean & Transform

→

ModelTrain & Validate

→

DeployServe & Scale

→

MonitorTrack & Improve

06 / Writing

Thinking beyond
the model.

Technical writing on applied machine learning, data science, reproducibility, and the engineering decisions that turn models into reliable systems.

Preventing Data Leakage in Machine Learning

Machine Learning · Data Quality

Planned

From Notebook to Production: Building Reproducible ML Pipelines

MLOps · ML Engineering

Planned

Forecasting Real-World Demand: What Makes Time-Series ML Different

Forecasting · Applied ML

Planned

07 / Contact

Let’s build
something
that matters.

Have an opportunity, a question,
or just want to say hello?
I’d love to hear from you.

suhailkhan.dev@outlook.com +1 (929) 350-3989New York, NY, United States

I’m interested in full-time roles across data science,
machine learning engineering, healthcare AI, cloud AI,
and product technology teams in the United States.

LinkedIn ↗GitHub ↗Resume ↗

Building intelligent systems from data.

Tools & Technologies

Data science, built for real systems.

Master of Science in Information Technology

Bachelor of Engineering in Computer Science & Engineering

Work at the intersection of data, systems, and care.

Data Scientist

Junior Data Analyst

Data Analyst I

Leadership beyond the role.

President

ACE Lords — Department of Computer Science & Engineering

From complex datato intelligent systems.

30-Day Hospital Readmission Risk Prediction

From modelingto production.

Built for real-world systems

Programming

Machine Learning & Data Science

MLOps & ML Engineering

Data & Visualization

Cloud & Production

The stack in action

Thinking beyondthe model.

Preventing Data Leakage in Machine Learning

From Notebook to Production: Building Reproducible ML Pipelines

Forecasting Real-World Demand: What Makes Time-Series ML Different

Let’s buildsomethingthat matters.

Building intelligent
systems from data.

Data science, built
for real systems.

From complex data
to intelligent systems.

From modeling
to production.

Thinking beyond
the model.

Let’s build
something
that matters.