I work at the intersection of applied AI, data science, and telecommunications, focusing on problems where the answer has to work beyond a notebook. My work spans network analytics, anomaly detection, forecasting, root-cause analysis, automation, and large-scale data platforms, with one recurring objective: turn complex operational data into decisions that engineers and stakeholders can actually use.
I tend to question the data before I question the model. A technically correct analysis can still produce the wrong conclusion when the aggregation level, KPI definition, population, time window, missing data, or denominator is wrong. I therefore spend significant effort validating assumptions, understanding how the data was generated, and separating statistical patterns from operational reality before optimizing an algorithm.
A Practical ExampleA recurring example in telecom analytics is subscriber aggregation. Counting unique users across an entire reporting period may look mathematically valid, but it can hide daily behavior and produce misleading comparisons between sites, technologies, or device segments. In such cases, I first reconstruct the metric at the correct daily and network granularity, validate the population and denominator, and only then aggregate it into a management-level view. Small choices in analytical framing often change the conclusion more than changing the model itself.
Principles
Start with the decision, not the model.
The problem, constraints, and action that follows should determine the technical approach—not the other way around.
Interrogate the data before trusting the metric.
Definitions, granularity, missingness, population bias, and aggregation can invalidate an otherwise correct analysis.
A model is not finished when the notebook works.
Reliability, scalability, monitoring, reproducibility, and integration are part of the solution.
Be precise about what the evidence supports.
I prefer an explainable limitation over a confident conclusion that the data cannot justify.
The strongest solutions cross disciplinary boundaries.
Domain knowledge, data engineering, machine learning, and operational context should reinforce each other rather than operate in isolation.
My principle is simple: an AI or analytics solution is valuable only when its assumptions are defensible, its results are reproducible, and its output leads to a better decision.