Browse Skills

Guides teams through MongoDB Atlas production deployments — cluster architecture, performance optimization, Atlas Search, security hardening, backup strategies, and cost management for cloud-native document database workloads.

Guides decisions on when and how to fine-tune large language models, covering LoRA/QLoRA, dataset curation, RLHF/DPO alignment, evaluation strategies, and cost-performance tradeoffs for production deployments.

Design and build production-grade n8n automation workflows integrating LLMs, RAG pipelines, and AI agents with robust error handling and cost optimization.

Build, evaluate, and deploy machine learning models directly in Google BigQuery using SQL, from classification and regression to time-series forecasting and recommendations.

Guides the end-to-end deployment of TensorFlow Lite models on mobile and edge devices — from model conversion and quantization to on-device inference optimization, hardware delegate selection, and production monitoring. Ensures models meet latency, size, and accuracy constraints for resource-constrained environments.

Design BI architectures with semantic layers, dimensional models, self-service analytics, KPI frameworks, and modern data stack patterns using dbt, Looker, Metabase, and Tableau.

Conduct environmental lifecycle assessments (LCA) of products following ISO 14040/14044, covering goal definition, inventory analysis, impact assessment, and improvement recommendations for sustainable product design.

Designs comprehensive data labeling strategies including annotation pipeline architecture, inter-annotator agreement measurement, active learning loops, label quality control, crowdsourcing management, few-shot labeling, and weak supervision techniques.

Optimize BigQuery deployments for maximum performance and minimum cost through partitioning, clustering, query tuning, and systematic cost control strategies.

Design and implement automated data quality validation pipelines using Great Expectations, with expectation suite design, threshold calibration, and integration into dbt, Airflow, and Spark workflows.

Design, implement, and operate real-time event streaming systems using Redis Streams. Covers consumer group topologies, failure recovery with XAUTOCLAIM, memory management, persistence configuration, and architecture comparisons with Kafka and RabbitMQ.

Guides geospatial data analysis including GIS fundamentals, PostGIS spatial queries, coordinate systems, geocoding, spatial indexing, and map visualization. Helps design location intelligence solutions and spatial analytics workflows.

Design and deploy Statistical Process Control systems with proper control chart selection, rational subgrouping, process capability analysis, and automated monitoring to detect and reduce process variation in manufacturing and service operations.

Expert guidance for building high-performance analytical queries and data pipelines using DuckDB, the embedded OLAP database engine with columnar storage and vectorized execution.

Helps you set up and optimize MLflow for experiment tracking, run comparison, model registry management, and production MLOps workflows across any ML framework.

Designs rigorous experiments — from clinical RCTs to A/B tests to industrial DOE — with proper randomization, power analysis, control structures, and statistical validity to produce actionable causal evidence.

Expert in feature store design and operations — real-time feature serving, offline/online consistency, feature pipelines, and ML platform integration with Feast, Tecton, and custom solutions.

Expert guidance for selecting, fine-tuning, and deploying transformer-based NLP models using the Hugging Face ecosystem and PyTorch for production natural language processing tasks.

Bayesian statistician for measuring true incremental marketing impact — campaign lift estimation, Marketing Mix Modeling, geo-based incrementality experiments, and Bayesian A/B testing with properly quantified uncertainty.

Design dimensional models, data vault architectures, and analytics schemas optimized for Snowflake with Kimball methodology, dbt integration, and platform-native performance tuning.

Designs production-grade drift monitoring pipelines for ML models, covering data drift, concept drift, and prediction drift detection with statistical methods, alerting thresholds, and automated response procedures.

Comprehensive methodological guidance for grounded theory research across all three major traditions (Glaserian, Straussian, Constructivist), covering research design, theoretical sampling, coding, memo writing, and theory construction.

Senior MLOps drift detection specialist that designs production-grade monitoring pipelines with statistical tests, alerting thresholds, and automated retraining triggers.

Designs and enforces formal data contracts between producer and consumer teams, implementing schema governance, automated quality testing, and graduated enforcement strategies to ensure upstream data discipline.