I work in systems analysis across data, operations, and human-centered environments where decisions must hold up under constraint, ambiguity, and imperfect information.

My focus is on understanding the upstream conditions that shape downstream outcomes. In practice, this means working with data that is incomplete, delayed, or distorted by the systems that produce it, including healthcare, education, and regulated settings.

Rather than optimizing metrics or models in isolation, I aim to reduce decision risk by identifying where assumptions, proxies, or statistically “better” models create confidence that is not supported by operational reality. I am especially interested in failure modes where analytical rigor masks fragility instead of revealing it.

This GitHub is not a gallery of experiments or optimization exercises.

Some work is published under corporate ownership and is intentionally not mirrored here.

Repositories developed under Right Business Pte. Ltd. are maintained separately: https://github.com/RightBusiness

It is a record of how I:

• Reason under operational and data constraints
• Evaluate analytical trade-offs and failure modes
• Prioritize interpretability, stability, and decision integrity over superficial performance

Some repositories are technical. Others focus on analytical judgment.
The unifying theme is process integrity over headline metrics.

📌 https://github.com/GazaliAhmad/diabetes-ml-faceoff

This case study examines model selection in a healthcare-adjacent context where interpretability, stability, and decision risk matter more than marginal accuracy gains.

The work documents:

• How failure modes and interpretability shaped the final model choice
• Why statistically attractive models were rejected due to risk and fragility
• How small, ambiguous datasets change what “good” modeling actually means in practice

The emphasis is not on model performance alone, but on whether the model’s behavior would remain defensible under real-world scrutiny.

This repository best reflects how I make analytical decisions when outcomes matter.

The following repositories provide supporting context for my analytical and systems capability:

Demonstrates how variables gain meaning only when interpreted within economic and social context, rather than treated as isolated predictors.

Examines global COVID-19 datasets to identify reporting distortions, boundary misalignment, and false causal assumptions commonly produced by public health data.

The analysis highlights how delayed disclosure, administrative aggregation, and proxy variables (e.g. hospital beds, smoking prevalence) can generate misleading conclusions if treated as direct epidemiological signals.

The emphasis is on preventing confident but incorrect conclusions, rather than maximizing descriptive completeness.

Explores behavioral constraints, guardrails, and controlled interaction in LLM systems, with an emphasis on safety, failure modes, and predictable system behavior.

These projects are not presented as highlights, but as evidence of breadth, execution, and judgment across domains.

My background spans frontline operations, enterprise systems support, system integration, and applied analytics.

This trajectory is intentional. It is why I treat data as something generated by systems and human behavior, not as an abstract artifact detached from operational reality.

I am open to roles involving:

• Systems Analysis
• Applied analytics in operational or regulated environments
• Context-heavy analytical work where judgment, constraint, and decision integrity matter

• LinkedIn: https://www.linkedin.com/in/gazaliahmad/
• Email: gazali.ahmad@outlook.com