Data Science & ML Habilidades de Claude

Azure AI Voice Live for .NET

Enables real-time, bidirectional voice AI capabilities in .NET applications using Azure AI and WebSocket communication.

Business Analyst

Transforms complex data into actionable business intelligence through advanced analytics, KPI frameworks, and predictive modeling.

FootballBin Match Predictions

Retrieves AI-powered football match predictions, scores, and statistics for the Premier League and Champions League.

Excel & Spreadsheet Automation

Automates the creation, editing, and analysis of Excel spreadsheets with professional formatting, formula integrity, and financial modeling standards.

FootballBin Sports Predictions

Provides AI-powered football match predictions and statistics for the Premier League and Champions League directly within Claude Code.

FootballBin Match Predictions

Provides AI-powered predictions and analytics for Premier League and Champions League matches directly within the Claude Code environment.

FootballBin Match Predictions

Provides AI-powered football match predictions and performance analytics for the Premier League and Champions League.

Trading Backtesting Frameworks

Builds robust, bias-aware backtesting systems to validate quantitative trading strategies and produce reliable performance estimates.

Apache Spark Optimization

Optimizes Apache Spark jobs through advanced partitioning, memory management, and shuffle tuning strategies.

ML Pipeline Workflow

Orchestrates end-to-end MLOps pipelines from data preparation and model training through to production deployment and monitoring.

LangChain Architecture Design

Architects sophisticated LLM applications using LangChain's modular framework for agents, memory, and complex workflows.

LLM Evaluation Framework

Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and systematic benchmarking.

Vector Index Tuning

Optimizes vector index performance for production environments by balancing latency, recall, and memory usage.

Data Storytelling for Analytics

Transforms raw data and complex analytics into persuasive business narratives and structured executive presentations.

Prompt Engineering Patterns

Optimizes LLM performance and reliability through advanced prompting techniques like few-shot learning, chain-of-thought, and modular templates.

Embedding Strategies for RAG

Optimizes embedding model selection and chunking strategies for semantic search and Retrieval-Augmented Generation (RAG) applications.

Risk Metrics Calculation

Calculates comprehensive portfolio risk metrics and performance indicators for quantitative finance and investment management.

Hybrid Search Implementation

Combines vector similarity and keyword-based search to improve retrieval accuracy and recall in RAG systems.

Vector Search & Similarity Patterns

Implements efficient similarity search and vector database patterns for semantic retrieval and RAG systems.

RAG Implementation Framework

Builds robust Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search to ground AI responses in custom knowledge.

Hybrid Search Implementation

Implements search architectures that combine vector similarity and keyword matching to maximize retrieval precision and recall.

LangChain Architecture

Builds sophisticated LLM applications by architecting modular chains, autonomous agents, and robust memory management systems using the LangChain framework.

Similarity Search Patterns

Implements efficient similarity search and vector database patterns for semantic search, RAG retrieval, and recommendation engines.

Prompt Engineering Patterns

Optimizes LLM performance and reliability through advanced techniques like few-shot learning, chain-of-thought reasoning, and structured prompt templates.

Backtesting Frameworks

Builds robust, production-grade backtesting systems for trading strategies while mitigating common pitfalls like look-ahead and survivorship biases.

ML Pipeline Workflow

Build and orchestrate production-grade MLOps pipelines from data preparation through model training, validation, and deployment.

RAG Implementation

Builds Retrieval-Augmented Generation (RAG) systems for LLM applications using vector databases, semantic search, and advanced retrieval strategies.

Embedding Strategies

Optimizes semantic search and RAG applications through intelligent model selection, chunking strategies, and embedding quality evaluation.

LLM Evaluation

Implements comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking to ensure performance and quality.

Spark Optimization

Optimizes Apache Spark workloads through advanced partitioning, memory management, and shuffle tuning strategies.