Gökhan Çetinkaya — Machine Learning & AI Consultant

What I do

I have 25+ years of professional experience, from leading software teams to running my own data science consultancy. For more than a decade, I’ve been designing and delivering ML/AI solutions that are scientifically sound and commercially useful.

Typical ways I work with clients

• AI & ML strategy. Clarifying where AI can realistically move the needle for your business, and where it cannot.
• Solution design & prototyping. Framing problems, designing data/ML pipelines, and building proof-of-concepts that stakeholders can actually test.
• Production-oriented modeling. From classical ML to modern LLMs and agentic systems, with attention to reliability, data quality, and monitoring.
• Advisory & mentoring. Helping teams think clearly about metrics, experimentation, and the trade-offs between complexity and robustness.

How I think about ML & AI

My bias is simple: AI must earn its place by delivering value. That means we start from the problem and constraints, not from a specific model or trend.

• Science as a guide. Scientific thinking, proper baselines, and honest evaluation before ambitious claims.
• Simplicity first. Prefer simpler models and systems when they solve the problem adequately — sophistication only when it truly buys us something.
• End-to-end view. From data collection and labeling to deployment, observability, and iteration — not just modeling in isolation.

Areas I often work in

• Forecasting & Optimization. Demand forecasting, labor planning, inventory optimization, and scenario modeling for operational decisions.
• Pricing & Revenue Intelligence. Dynamic pricing models, elasticity estimation, and financial impact simulations.
• Customer & Behavioral Analytics. Segmentation, churn prediction, personalization, and recommender systems.
• Applied ML Systems. End-to-end pipelines for classification, prediction, simulation, and decision automation across retail, finance, and SaaS.
• Decision support with LLMs. Question-answering and agentic systems on top of structured and unstructured internal data.
• Optimization & simulation. Combining ML, optimization, and domain knowledge to support strategic planning and resource allocation.

My work spans multiple sectors, but the common thread is pragmatic, value-focused AI that improves decisions and outcomes.

Writings

Occasional notes on applied ML, uncertainty, and decision-making.

• From Models to Decisions: A Practical Mental Model for Applied ML
  A conceptual framework separating prediction, uncertainty, dynamics, and decisions in real-world ML systems.
• Simulation: The Missing Layer Between Models and Decisions
  Why feedback loops, delays, and tail risk often dominate outcomes — and why simulation belongs between models and real decisions.
• Why Uncertainty Matters More Than Accuracy
  A practical essay on why point forecasts are often insufficient, and how uncertainty reshapes real business decisions.
• Why Accuracy Is Not Enough
  Why many ML projects fail at impact: model metrics aren’t business KPIs, and point forecasts aren’t decisions.
• Statistical / Bayesian Inference vs. Machine Learning: Rivals or Teammates?
  A short essay on prediction, explanation, and what we actually know.

Currently

I’m focused on helping companies:

• Turn existing data into actionable operational insights.
• Experiment responsibly with LLM-powered assistants and agentic workflows.
• Build lean, explainable ML solutions that teams can own and maintain.