Selected projects and case studies from the fields of Data Science, Machine Learning, and data analytics. Each project showcases a specific problem, the solution used, and measurable results.
Results in Numbers
| Metric | Value |
|---|---|
| Completed projects | dozens |
| Production ML models (Unified Pipeline) | ~100 |
| Years of DS/ML experience | 7+ |
| Average model accuracy improvement | 15% |
| Model deployment time reduction | up to 80% |
Selected Projects
⚙️ Unified Pipeline
Problem: Fragmented ML processes, inconsistent model quality, and long deployment times (10+ days).
Solution: Design and implementation of a unified ML pipeline on the Databricks platform with automated feature engineering, model registry, and A/B testing.
Results:
| Metric | Before | After | Improvement |
|---|---|---|---|
| Model deployment time | 10 days | 2 days | −8 days |
| Model accuracy | baseline | +15% | +15% |
| Pipeline runtime | 4 hours | 2.4 hours | −40% |
Technologies: Python, PySpark, Databricks, MLflow, Optuna, Docker
🤖 Open Source Projects
- MCP Prompt Broker – routing of instruction profiles for AI agents based on prompt type
- PENB Energy Label Approximation – modeling apartment energy performance and consumption simulation
- WireGuard in Docker – VPN + Nginx reverse proxy architecture for secure service publishing
Areas of Expertise
- Banking and finance: Propensity models, CRM optimization, NBO, dynamic pricing, product analytics
- MLOps: ML pipeline automation, deployment, monitoring, model registry (MLflow)
- Big Data: PySpark, Databricks, Spark, Hadoop, Hortonworks, Oracle DBMS, distributed computing
- BI & Reporting: Power BI, MS Excel, PL/SQL analytics, data transfers and integrations
- Econometrics: Macroeconomic analyses, forecasting, time series
- AI Engineering: MCP, LLM integration, RAG pipeline, AI implementation in corporate environments
Interested in What I Can Do for You?
Every project starts with a free introductory consultation. Together we will find out where data and AI can help you the most.