Browse Skills

Create publication-quality static figures with Matplotlib and interactive web-ready visualizations with Plotly, guided by visual perception research and data storytelling principles.

Designs and implements enterprise skills taxonomies and competency frameworks that enable skills-based workforce planning, internal mobility, gap analysis, and learning path mapping using validated I/O psychology methodologies.

Guides the design of reinforcement learning systems including policy selection (PPO, SAC, DQN), environment design, reward shaping, exploration strategies, multi-agent configurations, and sim-to-real transfer for robotics and game AI.

Expert guidance for building and optimizing Apache Spark data processing pipelines, from PySpark development to cluster tuning and lakehouse architecture.

Design and implement anomaly detection systems for tabular data, time series, and streaming environments. Guides algorithm selection, feature engineering, threshold tuning, and production deployment with focus on minimizing false positives.

Designs and implements graph neural network architectures for relational and structured data problems. Covers GCN, GAT, GraphSAGE, knowledge graphs, message passing, and graph-level tasks including node classification, link prediction, and graph generation.

Expert guidance for maximizing LLM context window effectiveness through token budgeting, prompt compression, information placement, caching strategies, and cost-efficient context management.

Expert in MongoDB production systems — document modeling, aggregation pipelines, sharding, indexing strategy, Atlas operations, and migration from relational databases.

Production-grade machine learning pipeline engineering with scikit-learn. Covers preprocessing pipelines, feature engineering, model selection, cross-validation, hyperparameter tuning, serialization, and deployment patterns for reliable ML systems.

Design effective chain-of-thought prompting strategies that elicit step-by-step reasoning from large language models for complex problem-solving, analysis, and decision-making tasks.

Design production-grade hybrid search systems that combine BM25 keyword search with vector-based semantic retrieval, multi-stage reranking pipelines, and RAG retrieval layers for optimal relevance and recall.

Design and implement cohort analyses for retention measurement, behavioral segmentation, and product analytics. Provides SQL implementations, visualization recommendations, and statistical interpretation guidance.

Expert guidance for building comprehensive observability into LLM-powered applications — covering tracing, cost tracking, quality monitoring, latency analysis, and automated alerting across model providers.

Expert architect for designing multi-step prompt chains and orchestration patterns — from task decomposition and chain-of-thought sequencing to intermediate verification gates, context window management, error recovery strategies, and production-grade prompt pipeline design for complex LLM workflows.

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 and implements sophisticated Airtable automations, scripting extensions, and integration workflows that transform manual business processes into reliable, scalable automated systems.

Design synthetic data generation pipelines that produce privacy-preserving, statistically faithful datasets for ML training, testing, and data sharing using GANs, copulas, and differential privacy.

Designs production-grade Retrieval-Augmented Generation pipelines including chunking strategies, embedding model selection, vector database architecture, hybrid search, reranking, and hallucination reduction techniques.

Expert in causal graph modeling using Directed Acyclic Graphs (DAGs), structural causal models, do-calculus, and causal discovery algorithms. Guides practitioners through identification strategies, quasi-experimental designs, and implementation with DoWhy, CausalNex, and causal-learn.

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.

Builds structured competitive intelligence analyses including competitor profiling, feature parity matrices, win/loss patterns, pricing intelligence, and sales battlecards using proven CI frameworks and market research methodologies.

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

Expert in Google BigQuery — SQL analytics at scale, partitioning and clustering strategies, BigQuery ML, materialized views, Dataform pipelines, cost optimization, and data governance.

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.