PhD
What I do: Review your data split and validation strategy Detect potential data leakage Check for overfitting / unstable performance Assess metric interpretation Provide 3–5 concrete improvement actions Who this is for: Early-stage startups building predictive models Founders using ML but unsure about robustness Researchers preparing for submission CTOs wanting a second technical opinion
What I do: -Evaluate cross-validation design -Assess generalization strategy -Review resampling framework -Suggest robust validation improvements -Provide short technical report Industries where this fits well: -Healthtech -fintech risk modeling -Industrial predictive maintenance - Energy load forecasting AI-based SaaS tools Computer vision startups Biotech / diagnostics Climate modeling startups
What I do: - Reliability diagnostics - Variance & instability assessment - Stress-testing framework - Confidence interval validation - Failure mode analysis Industries where this is valuable: - Medical AI / digital health - Financial risk scoring - Insurance underwriting models - Autonomous systems - Industrial safety systems - Environmental risk modeling - Pharma data science teams
{ "role": "ML Validation & Reliability Specialist", "services": [ { "name": "Model Sanity Check", "inputs": ["notebook/repo link", "data schema", "current metrics"], "outputs": ["top 5 risks", "fix plan", "revised validation outline"], "sla_hours": 24, "price_usd": 180 }, { "name": "Leakage & CV Audit", "inputs": ["repo", "split logic", "feature pipeline description"], "outputs": ["leakage verdict", "corrected split strategy", "audit report (PDF)"], "sla_hours": 72, "price_usd": 650 } ], "availability": "Fri 18-22, Sat 9-13 (America/Bogota)", "response_time_hours": 12 }
Clinical & High-Risk ML Validation Architect I help startups and research teams transform unstable AI models into regulator-ready, statistically robust systems.