Position: Data Scientist/Data Analyst/ ML Engineer
Category: Data Science
English level: intermediate
Years of experience: 2

I have experience in: classical  machine learning and classical ML algorithms on tabular data for resolving classification, regression, clusterisation and time series forecasting tasks, science statistics, advanced exploratory data analysis and model explanation tools; direct communication with the customer in the form of daily communication with him to set tasks and check the current status of the project.
     Also have deep financial analysis experience, credit process and financial systems knowledge.
     Familiar with digital product analysis (cohort analysis, user retention, A/B testing, marketing funnel metrics marketing sales pipe line metrics CPI, CPM, CPC, CPA, traffic  quality analysis).
Data scientist
Nov 2021-Present
Key competencies:
- business understanding
- tech task definition to solve a business problem
- production-quality code

ML competencies:
Full ml pipeline
- data ingestion and collection
- ml driven eda
- data preparation
- feature engineering
- modeling
- model evaluation
-use model 


Projects:
• Support Custom Value Management project (Frequency – increasing the frequency of purchases, Anti-churn – return churn customers) in big retailer:
-	Working with marketing campaigns using ML-models (uplift, recommendation model), gathering data from DWH with pipelines using MS SQL, retrain models.
-	 Post analysis of marketing campaigns results (gathering data from DWH, metrics calculation, statistical significance assessment of each campaigns - A/B test).
• Data analysis (RFM – Life cycle grid, survival curve, metrics selection to evaluate the success of marketing campaigns, prepare customers segmentation between cases, customers basket purchases).
• Improved existing SQL/Python scripts in project, improve ML-models.

Role: data scientist 
Technologies: Python (Pandas, Scikit-learn, Numpy, Gradient boosting libs, Mlxtend, Optuna, GridSearch, Seaborn, Matplotlib, Selenium, Prophet packages etc.), MS SQL server,  Power bi etc.

Credit risk analyst
Nov 2005-Oct 2021
Credit decisions acceptance, financial and credit risk analysis of corporate clients, loan application support in the credit process, branch network training and mentoring.
Prepared business requirements for fast credit process 
(description list of automated controls and checks).
Diagramming business process (credit functionality).   
UAT IT improvements.