For Hiring Managers
Fast fit-check for teams hiring an Applied AI/systems Leader, ML Engineer, or Manufacturing Analytics Lead.
Copy-Paste Candidate Summary
Jason T. Cole is a Staff+/Principal-level engineer specializing in applied AI, ML systems, and manufacturing analytics. 7+ years building production AI systems from early Transformer adoption to current agentic architectures. Proven metrics: 16+ hours/week saved per team member, >10% yield improvement in production, 60%+ reduction in defect crises. Available immediately for full-time or contract roles. Remote-friendly (UTC±2 overlap). Strong IC track record with optional leadership capability.
90-Second Decision Snapshot
- Business translator: turns unclear business pain into scoped analytics and ML initiatives that ship.
- Builder mindset: delivers APIs, data applications, AI workflows, and RAG assistants in production contexts.
- AI systems depth: ~7 years building language-model and retrieval systems, from early Transformer adoption to current agentic AI architectures.
- Current focus (since Oct 2025): full-time on agentic workflows and programming systems, including multi-agent orchestration and automation reliability patterns.
- Statistical enablement: coaches teams in practical statistics and JMP; invited industry expert contributor to the STIPS certification exam.
- Operational depth: combines statistical rigor with process engineering experience across global manufacturing environments.
- Execution focus: prioritizes measurable outcomes (quality, throughput, reliability, and adoption) over prototype-only outputs.
Role-Fit Matrix
Quick scan for role alignment. Check marks indicate strong fit.
Principal IC ✓
Strong FitPerfect fit — Staff+ level, technical depth, proven delivery in production environments.
Hybrid IC/Lead ✓
Strong FitCan mentor, architect, and deliver. Experience leading small teams while staying hands-on.
VP/Director ⚡
HybridCan scale to leadership but prefers IC focus. Open to discussing scope.
Senior ML Engineer ⚪
PartialStrong Python/data background, but primary focus is full-stack SWE/agentic systems.
Featured Case Studies
Representative delivery examples that show strategy, implementation, and outcomes.
EDA Data Platform
Program impact highlights include 16+ hours/week saved and defect reduction patterns from 5% to 1% in targeted workflows.
Generative AI Chatbots
Initial deployment covered 2 business functions (HR + Finance) with a reusable 1-pattern rollout model for future assistants.
Low-Frequency Ultrasound ML
ML-guided quality interventions aligned with >60% defect-crisis reduction and >10% yield improvement in related production programs.
Top 3 Quantified Wins
Public-shareable metrics that summarize delivery impact across operations and applied AI programs.
>60% Defect-Crisis Reduction
DMAIC and analytics-led interventions reduced major defect crises by more than 60% in targeted quality programs.
5% → 1% Defect Rate Shift
Nuisance material defect rates were reduced from 5% to 1% through tighter monitoring and process control loops.
>10% Yield Improvement
One-year process optimization efforts delivered more than 10% yield improvement in a production environment.
Good Interview Topics
If useful, we can quickly discuss these high-signal areas:
Production ML Roadmaps
How to prioritize use cases, sequence delivery, and de-risk deployment in operational environments.
Quality + Throughput Levers
Which analytics interventions move process performance fastest without disrupting plant operations.
Adoption + Capability Strategy
How to align engineering, operations, and leadership while raising team capability in statistics, JMP, and AI tool usage.
Open Source & Code
Public repositories showing applied AI systems, tooling, and ongoing development work.
eda_dataset_mcp
MCP server for exploratory data analysis — provides EDA capabilities through the Model Context Protocol for AI-assisted data investigation.
jupyter-dash
Develop Dash apps in Jupyter Notebook and JupyterLab — enables iterative development of interactive dashboards for analytics workflows.
More Repositories
Additional public repositories including portfolio materials, experiments, and ongoing development work.
Ready to move forward?
If you're hiring for outcomes-focused applied AI/systems leadership, ML engineering, or manufacturing analytics leadership, I'd welcome a conversation.
Next step: Send a short role brief plus 2–3 interview slots; I typically reply within 1 business day.