About

Building useful systems from complexity.

I work across software, analytics, data engineering, machine learning, and automationto turn messy real-world information into systems that are reliable, explainable, and genuinely useful.

My background sits at the intersection of analytics engineering, applied data science, and software development. I have built payroll and compliance systems, OCR extraction workflows, NLP experiments, forecasting models, geospatial analyses, dashboards, and decision-support tools across domains where the data is imperfect and the output has to stand up to real use.

I am most comfortable where the problem is complex, the data is messy, and the solution needs to be both technically sound and practically usable. That usually means translating rules into logic, designing reproducible pipelines, validating outputs carefully, and building systems that do more than just look impressive in a notebook.

Beyond work, I care about learning fast, thinking clearly, and building with intent. I enjoy travelling, exploring new ideas, and working on projects that connect technical depth with real-world usefulness.

Headshot of Hemang Sharma

Focus

Software, pipelines, analytics, machine learning, and AI systems.

Style

Calm design, measurable outcomes, and systems that can hold up in real environments.

Software & Automation

I build tools, workflow logic, and production-oriented systems that reduce manual work and improve reliability.

Data & Pipelines

I work across messy datasets, analytical workflows, validation layers, and reporting environments that need to scale.

Machine Learning & AI

From forecasting to OCR, NLP, and deep learning, I build systems that connect models to useful outcomes.

Tools & Platforms

Depth across the stack.

A working toolkit spanning data, analytics, engineering, cloud, BI, web, and AI — chosen for practicality, speed, and the ability to ship useful systems.

Tech Stack

Tools and platforms I use across software, analytics engineering, dashboards, machine learning, automation, and production-oriented delivery.