3. Data, Science & AI

Evaluates classification machine learning models and computes key performance metrics such as accuracy and F1 score.

Generates n-grams from text data for preprocessing in natural language processing tasks.

Specializes in AgentDB integration for RAN ML systems, optimizing vector storage, pattern recognition, and distributed training to achieve 150x faster search and 32x memory reduction.

Specializes in causal inference for RAN optimization using Graphical Posterior Causal Models to predict intervention effects and discover causal relationships in network data.

Optimizes network mobility using DSPy and LLM reasoning, focusing on handover management and temporal patterns for 15% performance improvement.

Provides AI-powered RAN quality monitoring with KPI tracking and intelligent assurance for 5G network performance management.

Enables research and development of machine learning algorithms for Radio Access Network optimization using reinforcement learning and causal inference models.

Optimizes prompts for Claude 4.5 using Anthropic's best practices, enhancing model performance through systematic improvements to CLAUDE.md, AGENTS.md, and skill files.

Provides AI-driven traffic forecasting and intelligent resource scaling for optimal 5G RAN capacity planning and management.

Analyzes Ericsson RAN features through AI-powered parameter correlation and MO class intelligence to optimize performance and enable intelligent feature management.

Optimizes RAN energy consumption using AI-driven predictive analytics and autonomous strategies to enhance sustainability and reduce operational costs in 5G networks.

Analyzes 5G RAN coverage through signal strength mapping and AI-optimized antenna parameter adjustments for intelligent network management.

Analyzes RAN performance data to detect bottlenecks, optimize network efficiency, and deliver deep insights using cognitive-temporal reasoning.

Optimizes Radio Access Networks using AI-driven swarm coordination and autonomous closed-loop cycles for self-healing and performance enhancement.

Designs scalable and reliable ETL/ELT pipeline architectures using industry best practices for data processing and transformation.

Reinforcement learning engineering for RAN systems using policy gradients, experience replay, and AgentDB for multi-objective optimization.

Optimizes 5G handover performance using predictive mobility control and cognitive management for seamless user experience.

Provides foundational PySpark utilities for distributed data processing and analysis in big data environments.

Automates web scraping and structured data extraction from websites, including JavaScript-heavy pages, with schema generation for efficient data pipeline building.

Optimizes data warehouse performance through query tuning, partitioning, and clustering for faster data retrieval and efficient storage.

Provides data quality testing with dbt tests and Great Expectations, including pipeline monitoring.

Designs optimized data schemas for analytics using dimensional modeling and normalization techniques.

Extracts text and tables from PDF documents for data processing and analysis tasks.

Integrates Google Gemini AI to analyze large codebases, verify implementations, and understand architecture using file inclusion syntax.