Skills

Designs trustworthy product analytics events, properties, identity rules, validation, privacy boundaries, dashboards, and governance for product and growth decisions.

Defines versioned data contracts between producers and consumers, with ownership, schemas, quality expectations, compatibility rules, and operational change management.

Turns analysis results into a clear narrative for a specific audience. Selects the key message, orders findings for impact, recommends the right chart for each point, writes plain-language takeaways, and frames actionable recommendations. Helps analysts move from raw numbers to a memo or slide narrative executives can act on.

Generates and refactors dbt models for analytics engineering. Writes staging, intermediate, and mart models following layered conventions, adds schema.yml tests and descriptions, applies incremental and materialization strategies, and structures sources and refs correctly. Produces SQL plus YAML that fits dbt best practices and is ready to run.

Inspects messy tabular data and produces a repeatable cleaning plan plus code. Detects and fixes common issues: inconsistent types, duplicate rows, missing values, malformed dates, mixed encodings, whitespace and casing problems, and outliers. Outputs pandas or Polars code, a summary of changes, and a validation checklist.

Defines data contracts between producers and consumers to prevent breaking changes in pipelines. Covers schema definitions, semantic types, freshness and quality SLAs, ownership, versioning, and backward/forward compatibility rules. Generates contract specs (e.g., ODCS-style) and CI checks that fail builds when a producer violates the contract.

Designs data quality checks for tables, pipelines, and warehouses. Generates expectation suites covering schema conformance, null and uniqueness constraints, referential integrity, freshness, and statistical drift, then wires them into pipelines so bad data is caught before it reaches dashboards or models.

Helps design and implement features for machine learning models from raw tabular, time-series, or text data. Suggests transformations, encodings, aggregations, and leakage-safe splits, explains the rationale, and generates reproducible feature pipeline code with validation.

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.

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.

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.

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.

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.

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.

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.

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.