Platform strategy
with delivery teeth
This is not advisory theater. The work covers governance, ML platforms, regulated analytics, and delivery recovery, but always from the same posture: own the architecture, align the operators, and ship the outcome.
50+
12
$30M+
90 Days
Where I take direct ownership
The scope is broad, but the standard is consistent: each capability must reduce risk, increase operational leverage, or create a measurable decision advantage.
GOVERNANCE
Data Governance & Compliance
Policy-as-code for HIPAA, GDPR, and SOX. Automated controls, lineage tracking, audit-ready documentation.
FROM
Fragmented compliance, manual audits, regulatory risk
TO
Automated policy enforcement with structured audit trails
- Structured audit trails
- Multi-jurisdictional data residency
- Automated policy enforcement
MLOPS
ML Platform Engineering
Experiment tracking to production inference. Model registries, automated retraining, CI/CD, monitoring.
FROM
Slow model deployment, scattered experiments, reliability issues
TO
Standardized CI/CD for ML with model registry and monitoring
- Standardized CI/CD for ML
- Feature store & model registry
- Real-time monitoring & observability
HEALTHCARE
Healthcare Data & Analytics
HIPAA-compliant pipelines for patient risk scores, capacity forecasts, PHI-safe executive dashboards.
FROM
Siloed PHI, compliance concerns, slow insights
TO
Real-time patient outcomes modeling with automated PHI protection
- Real-time patient outcomes modeling
- Automated PHI classification & protection
- Network capacity & access planning
How the work gets structured
The format changes based on urgency and internal maturity, but each engagement has a fixed operating cadence, decision owner, and proof of progress.
Fractional Leadership
Executive-level direction for teams that need operating cadence, architectural decisions, and stakeholder alignment without adding a permanent headcount immediately.
Platform Buildout
Design and delivery of data platforms, AI services, or governed analytics environments with practical implementation detail, not abstract target-state diagrams.
Program Recovery
Resetting stalled data and AI initiatives by narrowing scope, restoring ownership, and reconnecting the roadmap to measurable business outcomes.
Operating model
Sequence the work so momentum appears early.
Most data programs fail because they try to fix trust, tooling, and team behavior in the wrong order. The playbook is deliberately narrow.
Discover the real constraint: decision bottleneck, compliance risk, reliability gap, or organizational drift.
Sequence quick wins and structural fixes together so momentum appears early and compounds over the next quarter.
Leave behind instrumentation, ownership, and workflows that survive after the engagement ends.
Next Move
If the work matters, make the scope concrete
The fastest path is a short discovery pass with the real constraints on the table: timeline, regulatory pressure, technical debt, and what has already failed.