About
Commercial operator → analytics architect → applied AI leader. 13+ years across frontline sales, analytics architecture, and applied AI means I usually see the decision failure behind the dashboard, not just the dashboard itself.
Quick Facts
I'm Jason Rae — a commercial analytics and applied AI leader based near Stuttgart, Germany, with 13+ years across commercial leadership and analytics, pricing, forecasting, margin analysis, CRM governance, and decision-system repair. My edge is that I understand both sides of the problem: the commercial reality executives care about, and the technical systems needed to make the numbers trustworthy.
I have built sales functions, owned growth targets, turned around underperforming teams, corrected broken reporting logic, fully handled two acquisitions, and rebuilt the commercial foundations leaders rely on. When the problem calls for it, I build with Power BI, Python, SQL, machine learning, and LLM APIs. That combination of commercial credibility and technical depth is what sets my work apart. I have spent 10+ years building with TensorFlow since 2016, so I do not approach AI as a recent badge added to a consulting profile. I understand how the systems work because I build them.
That acquisition work included credits handling, refactoring data to work cleanly at customer and account level, and coordinating each country to review matching acquired customers against our own databases so information transfer and performance measurement stayed reliable.
Today, I focus on commercial decision intelligence: gross margin walks, pricing analytics, forecasting, opportunity identification, CRM quality, business reviews, workflow automation, and applied AI where it improves decision quality. I work where Sales, Finance, BI, IT and leadership intersect — especially where broken data, unclear ownership or inconsistent logic distort business decisions.
Outside my corporate role, I build production-grade systems that prove technical depth: algorithmic trading model pipelines, document-intelligence workflows, agent orchestration, multilingual SaaS, and smart home automation. That build capability matters because clients need someone who can both diagnose the commercial problem and ship the mechanism behind the fix.
In addition to public proof-of-work, my private portfolio spans 13 applied AI, analytics, automation, and trading-system repositories, including multi-repo ML research, governance tooling, and production-style workflow orchestration.
I care about decision quality. Most businesses do not struggle because the dashboard is missing; they struggle because logic, ownership, and governance break before technology is chosen. I fix that foundation, then use BI, automation, or applied AI where it genuinely improves speed, control, or confidence.
Building commercial decision systems across pricing, margin, forecasting, CRM, incentives and executive reporting. Governing BI platforms with standardized KPIs, semantic models and auditability.
Rebuilding data infrastructure and reporting logic so leaders get one trusted view of revenue, margin, pricing, and opportunity performance. Strong in Power BI semantic models, DAX, data governance, and legacy-report modernization.
Using Python, SQL, and TensorFlow-based modeling for forecasting, pricing, classification, and data-quality control. This is practical implementation work, not theory: writing code, validating outputs, and tying model behavior back to business decisions.
Director/SVP-level decision support, business review ownership and cross-functional rescue missions. Translating complex data-quality and forecast issues into executive-ready actions.
Bringing LLM and AI systems into commercial environments with validation, auditability, and clear use-case logic. The goal is reliable workflow improvement, not novelty.
Realigning CRM and forecasting processes, improving forecast accuracy accountability and correcting broken pricing and margin logic across multi-market commercial environments.
Every system starts with the business problem executives actually care about: margin, forecast accuracy, pricing discipline, CRM quality. I measure success by whether decision-making improves, not by technical complexity.
Broken logic, FX errors, mis-attributed margin drivers — these distort decisions before AI even enters the picture. I fix the foundation before building on top of it.
I work where Sales, Finance, BI, IT and Supply Chain intersect. I translate technical reality into business language and ensure alignment at every level — including Director and SVP.
Military training shaped my approach: precision, systems thinking and decision-making under pressure. I build AI and analytics systems that work in messy, political, real-world environments.
Best Next Step
If the issue is forecast trust, pricing logic, margin visibility, CRM governance, reporting sprawl, or an unclear AI-software decision, the right first move is a fit call or Diagnostic Review. Use this page and the resume as credibility support, not as the starting point for the commercial conversation.