About the service
I am a data analyst and machine learning engineer, and my job is to turn scattered raw data into solutions you can actually rely on. Most projects start the same way: a client brings a mountain of tables, database exports or event logs that nobody has really looked into, and asks a deceptively simple question — what happens next or which of these objects belong to one group. My job is to build that bridge. I clean and organize data using pandas and NumPy, figure out where it came from and where it lies, and only then build a model that deserves its place in production.
Over the years I've launched demand and revenue forecasts, churn and fraud classifiers, and recommendation systems that quietly boost engagement while the user doesn't even suspect the math running under the hood. I choose tools for the job, not for a pretty slide: scikit-learn for reliable classical baseline models, gradient boosting where the data asks for it, and deeper architectures only when they actually pay off. I care about honest evaluation — proper cross-validation, checking for leaks and metrics that reflect the true cost of error for the business, because a model that shines in a notebook and is useless in practice helps no one.
What clients value most — I don't hide behind jargon. Every project ends with something even a non-technical person can read: a clear report, a set of charts and an explanation in plain language of what the model can do and what it can't. Whether you need a one-time forecast, a reusable pipeline or a second opinion on an existing model, I'll carefully handle your data and return a result you can trust.
— Data cleaning and feature engineering in Python, pandas and NumPy
— Time series forecasting: demand, revenue, inventory
— Classification models: churn, fraud, lead scoring, risk assessment
— Recommendation systems and personalization
— Model training, tuning and honest evaluation with scikit-learn and gradient boosting
— Clear reports, dashboards and visualization for non-technical clients
Over the years I've launched demand and revenue forecasts, churn and fraud classifiers, and recommendation systems that quietly boost engagement while the user doesn't even suspect the math running under the hood. I choose tools for the job, not for a pretty slide: scikit-learn for reliable classical baseline models, gradient boosting where the data asks for it, and deeper architectures only when they actually pay off. I care about honest evaluation — proper cross-validation, checking for leaks and metrics that reflect the true cost of error for the business, because a model that shines in a notebook and is useless in practice helps no one.
What clients value most — I don't hide behind jargon. Every project ends with something even a non-technical person can read: a clear report, a set of charts and an explanation in plain language of what the model can do and what it can't. Whether you need a one-time forecast, a reusable pipeline or a second opinion on an existing model, I'll carefully handle your data and return a result you can trust.
— Data cleaning and feature engineering in Python, pandas and NumPy
— Time series forecasting: demand, revenue, inventory
— Classification models: churn, fraud, lead scoring, risk assessment
— Recommendation systems and personalization
— Model training, tuning and honest evaluation with scikit-learn and gradient boosting
— Clear reports, dashboards and visualization for non-technical clients
