data-science-engineering-foundation

Data Science and Engineering Foundation

Goal

Keep Data Science and Engineering Coach orchestration consistent across job changes and sessions without duplicating job-specific guidance. The coach loads this index at initialization and resume, then reads the reference for the current coaching moment.

Reference index

Reference	When to read
job-registry.md	Before offering or selecting a job, routing to a skill or specialist, or naming a durable output
lifecycle-classes.md	When starting, pausing, resuming, completing, or re-invoking a job
transition-protocol.md	When a topic shift, explicit request, or completion suggests moving between jobs
session-state.md	Before initialization, validation, mutation, resume, recovery, or reconstruction of coaching state
flow-state.md	Before interrupting work, crossing a gate, writing a durable artifact, or offering post-job choices

Success criteria

• Each orchestration rule has one owner in this package.
• State and lifecycle mechanics remain independent of job-specific methods.
• The coach reads the applicable reference before acting on its contract.

Constraints

• Keep catalog guidance in data-catalog, the durable data-catalog workflow for entities, declared relationships, lineage, and ERD-ready model semantics.
• Keep feasibility guidance in feasibility, the evidence-led data and ML feasibility-study workflow with lifecycle and interchange traceability.
• Keep pipeline and testing guidance in dataops, the DataOps reference for tier behavior, pipeline invariants, validation placement, tests, drift, and operational signals.
• Keep general experiment guidance in experiment-design, the reusable workflow for candidate selection, hypotheses, vetting, minimum scope, and result evaluation.
• Keep ML-specific experiment guidance in ml-experimentation, the reference for ML environments, reproducibility, tracking, evaluation, abstractions, and production readiness.
• Keep notebook and dashboard guidance in analysis-authoring, the reference for EDA notebook and analytical dashboard composition, visualization selection, and dashboard validation.
• Keep AI-system evaluation guidance in evaluation-design, the reference for evaluation dataset design, difficulty balance, metric selection, and tooling fit.
• Treat this package as internal foundation knowledge, not a user-selectable workflow.
• Preserve the skill authority boundaries and cross-cutting concerns defined in the job registry.