Browse Skills

Design and calibrate risk scoring systems that identify at-risk individuals within cohorts — enabling early interventions for student retention, customer churn prevention, patient readmission, and other predictive analytics use cases.

Expert guide for deploying TensorFlow models to production using TensorFlow Serving, covering SavedModel optimization, serving infrastructure, batching strategies, model versioning, and monitoring for reliable ML inference at scale.

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.

Evaluates machine learning models with rigorous methodology and statistical analysis. Covers metrics selection, cross-validation strategies, bias detection, A/B testing, confusion matrices, ROC/AUC analysis, and comprehensive evaluation reporting.

Design rigorous, bias-minimized surveys — from research objective clarification and question writing to sampling strategy, skip logic, and analysis planning.

Provides expert guidance on time series forecasting including statistical methods (ARIMA, ETS), Prophet, neural forecasting (N-BEATS, Temporal Fusion Transformer), seasonality decomposition, anomaly detection, and confidence interval estimation.

Guide the end-to-end curation of high-quality datasets for machine learning. Covers data collection strategies, cleaning pipelines, annotation workflows, bias auditing, deduplication, and dataset documentation following data-centric AI principles.

Designs optimal text chunking strategies for Retrieval-Augmented Generation (RAG) systems, balancing chunk size, semantic coherence, retrieval precision, and generation quality across diverse document types.

Design, tune, and evaluate search systems for maximum relevance using BM25, hybrid retrieval, learning-to-rank, and neural re-ranking with rigorous offline and online evaluation frameworks.

Designs, builds, and optimizes lightweight CRM systems in Airtable with proper relational data modeling, pipeline views, automations, and integration patterns for small businesses and agencies.

Optimizes ML model deployment for production inference including quantization (INT8/INT4), knowledge distillation, ONNX/TensorRT conversion, model pruning, batching strategies, and edge deployment for reducing latency and cost.

Design and implement digital twin systems that create virtual representations of physical assets using IoT sensor data, simulation models, and cloud platforms for predictive maintenance and operational optimization.

Designs comprehensive evaluation frameworks for LLM and AI systems, combining automated metrics (BLEU, ROUGE, BERTScore), LLM-as-judge patterns (G-Eval), RAG evaluation (RAGAS), standard benchmarks (MMLU, HumanEval), safety evaluations, and A/B testing methodologies into production-ready evaluation pipelines.

Design and optimize manufacturing facility layouts using digital twin simulation. Create virtual replicas of production floors to test layout alternatives, identify bottlenecks, and validate material flow improvements before physical implementation.

Designs privacy-preserving distributed machine learning systems using federated learning. Covers federated averaging, differential privacy, secure aggregation, communication efficiency, and compliance with privacy regulations.

Performance tuning for Pinecone, Weaviate, Qdrant, and pgvector including index selection, embedding dimensionality, chunking strategies, and hybrid search optimization for RAG systems.

Designs and optimizes retrieval-augmented generation systems for customer support operations, including knowledge base ingestion, chunking strategies, hybrid retrieval pipelines, answer generation with citation, and continuous quality improvement loops.

Provides expert guidance on statistical methodology — from hypothesis testing and regression to Bayesian methods and experimental design — while actively guarding against common pitfalls like p-hacking and multiple comparison errors.

Designs pedagogically sound automated feedback systems for programming education. Creates error taxonomies, scaffolded hint systems, and feedback message architectures that transform coding errors into effective learning opportunities.

Designs reverse ETL architectures to activate warehouse data in operational tools, covering Census/Hightouch patterns, sync strategies, audience building, and data activation use cases.

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 comprehensive evaluation systems for LLMs and AI applications, covering automated metrics, human evaluation protocols, LLM-as-a-judge pipelines, and CI/CD-integrated regression testing.

Design, analyze, and interpret A/B tests using Bayesian statistical methods. Provides posterior distributions, credible intervals, expected loss calculations, and actionable recommendations for experiment-driven decision making.

Design and implement machine learning-based lead scoring systems that predict conversion probability, prioritize sales efforts, and automate lead qualification using behavioral, firmographic, and engagement data from CRM platforms.