I am an ML Engineer and Data Scientist with 4+ years of client-facing experience in data migration and CRM analytics, and I am now focusing my career on machine learning engineering and data science. I build end-to-end ML systems and enjoy taking projects from data ingestion and profiling through model training, explainability, deployment, and monitoring.
I work in an async-first, remote-native style and am comfortable collaborating across CET and EST time zones. My background has helped me develop strong stakeholder communication skills, especially in distributed teams where clear written communication matters.
I have hands-on experience with FastAPI, SQLAlchemy, Optuna, SHAP, MLflow, Docker, GitHub Actions, and Kubernetes. I also pay close attention to data quality, including leakage detection, multicollinearity checks, and missingness analysis before training models.
My project work includes building a production ML experimentation platform, a zero-code AutoML web application, and several predictive analytics projects using Python, SQL, BigQuery, XGBoost, Streamlit, and Tableau. I enjoy creating practical tools that make machine learning more accessible and reproducible.
In my previous roles, I coordinated more than 100 data migration projects and worked closely with engineering and business stakeholders. That experience strengthened my ability to translate complex requirements into actionable technical solutions and deliver structured insights.
I am currently seeking a remote-first role as a Data Scientist, ML Engineer, or ML Researcher, ideally in an environment where I can continue building robust ML systems and contributing to data-driven product development.
Coordinated 100+ data migration projects across Help Desk, Project Management, and Accounting platforms. Used CRM analytics to segment 200+ accounts, track conversion funnels, surface upsell opportunities, and deliver structured data summaries. Partnered with engineering teams to translate customer requirements into technical specs and maintained long-term partnerships across EU and US markets.
Maintained network infrastructure, diagnosed hardware and software faults, and documented incidents. Built systematic, data-driven problem-resolution habits.
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