Skills

Generates realistic synthetic datasets that preserve the statistical properties and relationships of source data without exposing real records. Covers schema-aware generation, correlated and time-series fields, class balancing for ML training, and constraint preservation, with code for tools like SDV, Faker, or custom generators.

Builds retention and behavioral cohort analyses from event or transaction data. Defines cohorts by acquisition date or attributes, computes retention and churn curves, generates the SQL or pandas code to produce cohort tables, and interprets the results into actionable insights about engagement and lifecycle.

Extracts structured data from PDFs and scanned documents — invoices, receipts, forms, contracts, reports, and tables. Returns clean, typed output (JSON, CSV, or Markdown tables), handles multi-page layouts and nested tables, and flags low-confidence fields for review. Uses vision-capable models for image-based and scanned PDFs.

Builds and evaluates time series forecasting models using statistical and ML approaches. Supports ARIMA, Prophet, LSTM, Transformer-based models, and foundation models like TimesFM. Handles seasonality detection, trend decomposition, anomaly detection, multi-step forecasting, and backtesting with proper train/test splits for financial, IoT, and scientific time series data.

Sets up and manages machine learning experiment tracking, hyperparameter optimization, and model registry workflows. Integrates with MLflow, Weights & Biases, and Optuna for systematic experimentation. Handles metric logging, artifact storage, model versioning, reproducibility, and automated hyperparameter search with early stopping.

Analyzes complex networks and graphs using NetworkX, igraph, and PyTorch Geometric. Supports social network analysis, biological interaction networks, knowledge graphs, and citation networks. Performs community detection, centrality analysis, link prediction, graph neural networks, and network visualization with force-directed layouts.

Performs geospatial data analysis, mapping, and spatial statistics using GeoPandas, Shapely, Rasterio, and Folium. Supports vector and raster operations, coordinate transformations, spatial joins, buffer analysis, satellite imagery processing, choropleth mapping, and route optimization for GIS and remote sensing workflows.

Creates publication-quality scientific figures and plots using matplotlib, seaborn, and plotly. Supports common scientific plot types including heatmaps, volcano plots, survival curves, network graphs, phylogenetic trees, and multi-panel figures with proper statistical annotations, color-blind safe palettes, and journal formatting.

Designs and implements A/B testing experiments including hypothesis formulation, sample size calculation, variant configuration, metric definition, and statistical analysis planning. Covers both frontend feature flags and backend experiment frameworks.

Generates optimized SQL queries from natural language descriptions. Supports multiple dialects (PostgreSQL, MySQL, SQLite, SQL Server), handles complex joins, subqueries, window functions, and CTEs. Includes query explanation and performance optimization hints.

Analyzes datasets to extract insights, identify patterns, and generate visualizations. Supports exploratory data analysis (EDA), statistical testing, trend detection, and report generation. Works with CSV, JSON, and database outputs.