Problem
Telecom networks generate continuous, high-volume signals. Leaders need models that turn that signal into planning and performance decisions — not notebooks that never leave the lab.
My role
- Led data science delivery for network analytics use cases with clear decision owners.
- Aligned modeling work with pipeline, evaluation, and handoff realities at telco scale.
- Balanced sophistication with explainability and run-cost for operators.
Architecture (shape)
- Data plane — large-scale network / performance signals into reliable feature paths.
- Model layer — decision-support models tied to planning and performance KPIs.
- Decision surface — outputs owned by network / ops stakeholders, not orphaned dashboards.
- Feedback — evaluation against operational outcomes, not offline scores alone.
Approach
- Started from the decision and the owner, then worked backward to data and models.
- Emphasized pipelines, KPI-linked evaluation, and production handoff.
- Chose simpler, operable models when complexity did not buy decision quality.
Trade-offs
- Favored stable, explainable systems over black-box peaks that ops could not trust.
- Accepted engineering cost on data quality when it moved planning decisions.
- Scoped use cases tightly so delivery stayed owned end-to-end.
Evaluation
- Operational KPIs for planning / performance (stakeholder-defined).
- Stability under volume and refresh cadence constraints.
- Usability: could network teams act on the output without DS babysitting?
Outcome
Shipped decision-support ML grounded in large-scale telco data. This role sharpened how I design AI systems that must be reliable under real network and business pressure.
- Metric slot: e.g. coverage / cells / markets in scope — fill when shareable
- Metric slot: e.g. planning cycle time or KPI lift — fill when shareable
- NDA: Exact figures available in conversation under NDA where required.