Managed 60–75 inbound customer interactions per shift across multi-queue environments, applying SOP logic trees and BSS navigation to consistently achieve a 98% first-contact resolution rate.
Interpreted complex billing schemas and service entitlements across 40+ active plans, leveraging structured communication workflows to reduce recurring clarification requests by 32%.
Led 25+ weekly service migrations by analyzing eligibility matrices, usage behaviors, and catalog constraints while ensuring 100% CRM documentation and audit compliance.
Applied resolution scripts and escalation controls to maintain operational accuracy and service continuity in high-volume support environments.
Designed and deployed a scalable data ingestion architecture covering 150+ operational indicators using OPCUA and SCADA integrations, orchestrated through Python schedulers and Airflow DAG pipelines.
Developed and implemented a DBSCAN clustering model across 12 months of call records, isolating 5 anomalous routing groups using density-based segmentation and normalized feature engineering.
Led the end-to-end delivery of a revenue assurance platform within 9 months by defining 40+ KPIs through advanced SQL modeling and Power BI semantic layers, increasing anomaly detection coverage by 5%.
Established structured validation rules and analytical fact tables to ensure consistent data reliability, governance, and executive-level reporting accuracy.
Aggregated and transformed datasets from 20+ network elements using Python parsers, Shell schedulers, and SQL workflows to build normalized warehouse tables supporting daily operational reporting.
Architected a centralized KPI monitoring dashboard integrating 6 heterogeneous data sources (Oracle, PostgreSQL, MySQL, SQL Server), reducing mean incident resolution time by 50%.
Resolved 30+ annual cross-platform synchronization defects by conducting root cause decomposition and coordinating Level 3 support across dependent systems.
Implemented reconciliation checkpoints and structured governance controls to enhance data consistency and operational reliability across enterprise reporting layers.
Directed a cross-functional team of 5 engineers, allocating sprint workloads, supervising release governance, and enforcing deployment checkpoints to lower customer escalations by 28%.
Defined and monitored 30+ customer journey performance metrics using event correlation and failure-mode analysis, improving resolution predictability by 22%.
Coordinated 12 concurrent solution deployments by aligning technical specifications, interface contracts, and stakeholder requirements across internal and external partners.
Strengthened operational governance by implementing structured release controls and performance monitoring across service platforms.
Collaborated within a 6-member architecture task force to deliver 10+ annual service launches, optimizing workflow orchestration and activation state machines to improve service adoption by 20%.
Provided L1 and L2 operational support across 15 value-added service platforms, applying preventive maintenance cycles, log correlation, and capacity threshold monitoring to reduce unplanned outages by 18%.
Led 100+ incident investigations using SLA matrices and causal dependency graphs, implementing corrective action playbooks that reduced recurrence frequency by 24%.
Enhanced system scalability and resilience by refining orchestration workflows and enforcing structured capacity management practices.
Processed over 10M monthly CDR records across 8 technical platforms using Platine mediation, enforcing validation rules, duplication controls, and latency thresholds prior to warehouse loading.
Translated 15+ commercial offer definitions into OSS/BSS configurations by mapping rating logic, mediation flows, and billing cycles into technical service parameters.
Executed 20+ integration and functional testing campaigns using trace analysis and reconciliation reporting to stabilize post–go-live performance within SLA boundaries.
Strengthened billing accuracy and mediation reliability by implementing structured validation checkpoints and reconciliation controls across platforms.
Python, Machine Learning, Exploratory Data Analysis, Data Preprocessing, Model Evaluation
Conducted comprehensive exploratory data analysis (EDA) across 10+ IoT network datasets using descriptive statistics and visualization techniques to identify traffic trends, attack patterns, and underlying data quality issues.
Designed and executed data preprocessing workflows by handling missing values, resolving inconsistencies, and normalizing features, improving overall dataset reliability and analytical accuracy by 25%.
Implemented and evaluated multiple machine learning models using standardized 80/20 train–test splits to ensure unbiased performance assessment and reproducibility of results.
Compared performance across three classification algorithms using validation metrics, selecting the most effective model based on precision, recall, and F1-score benchmarks.
Achieved superior detection performance with a Random Forest model delivering an F1-score above 98% overall, while analyzing lower-performing minority classes (Metasploit Brute Force SSH – 88%, NMAP FIN SCAN – 91%) to assess class imbalance impact.
Available for data analytics, engineering, and automation opportunities across enterprise environments.
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