Data Science & ML Habilidades de Claude

Resampling Strategies for Tidy Models

Implements robust model validation and resampling techniques using the R tidymodels ecosystem and rsample package.

Health Economics Analysis in R

Performs advanced health economic evaluations including cost-effectiveness analysis, Markov modeling, and probabilistic sensitivity analysis in R.

Clinical Trials Statistical Analysis

Provides R-based statistical methods and best practices for clinical trial design, analysis, and regulatory reporting.

Document Ranking and Scoring

Ranks and filters retrieved documents based on vector similarity metrics to optimize RAG pipeline relevance.

FiftyOne Dataset Inference

Automates the creation of FiftyOne datasets from local media files and executes machine learning model inference pipelines.

FiftyOne Image Deduplication

Identifies and removes duplicate or visually similar images in FiftyOne datasets using deep learning embeddings.

RAG Systems Implementation

Builds robust Retrieval-Augmented Generation (RAG) systems for LLM applications using vector databases and semantic search.

LLM Evaluation & Benchmarking

Implements systematic evaluation strategies for LLM applications using automated metrics, human feedback loops, and benchmarking frameworks.

Prompt Engineering Patterns

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

Prompt Engineering Patterns

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

MLOps Pipeline Orchestrator

Build, automate, and manage end-to-end MLOps pipelines from data ingestion through production deployment.

Tidymodels Workflow Patterns

Implements end-to-end machine learning pipelines in R using the modern tidymodels ecosystem.

R Data Science & Testing

Streamlines R development using modern data analysis patterns, automated testing with testthat, and strict linting standards.

RAG Implementation

Implements Retrieval-Augmented Generation (RAG) systems with vector databases and semantic search to build grounded, knowledge-aware AI applications.

Financial Change-Point Detection

Detects structural breaks and regime shifts in financial time-series using Gaussian Process models to identify market transitions.

RAG Systems

Implements robust Retrieval-Augmented Generation (RAG) pipelines including document ingestion, hybrid search, reranking, and intelligent query routing.

LLM Evaluation Suite

Implements comprehensive evaluation frameworks for LLM applications using automated metrics, human feedback, and LLM-as-judge patterns.

LangChain Architecture & Agent Design

Architects sophisticated LLM applications using LangChain patterns for autonomous agents, conversational memory, and complex workflow orchestration.

LLM Application Evaluation

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

LangChain Architecture Design

Architects and implements sophisticated LLM applications using the LangChain framework for agents, memory management, and complex AI workflows.

MechInterp Feature Overview

Provides a rapid diagnostic summary of Sparse Autoencoder (SAE) features to generate research hypotheses and identify model behaviors.

Polars Data Processing

Provides expert guidance for high-performance data manipulation and ETL pipeline optimization using the Polars library in Python.

Gemini File Search Tool

Manages Google Gemini File Search stores and implements production-ready RAG systems for documents and codebases.

Mean-Variance Optimization

Calculates optimal portfolio weights and identifies the tangency portfolio to maximize the Sharpe ratio based on user-provided asset data.

Pixi Scientific Package Management

Manages fast, reproducible scientific Python environments by unifying the conda and PyPI ecosystems.

Modern R Development

Implements modern R programming practices, tidyverse patterns, and performance optimizations for data science workflows.

Autonomous Trading

Orchestrates end-to-end stock trading operations by performing automated screening, multi-model AI analysis, and order execution via Alpaca.

Automatic CSV Data Analyst

Analyzes CSV files automatically to provide statistical summaries, domain-specific insights, and relevant visualizations without requiring user intervention.

Prompt Engineering

Designs, optimizes, and troubleshoots large language model prompts using research-backed techniques and structured design patterns.

Prompt Engineering Patterns

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