Position: Leading Model Risk Specialist (Data Scientist)
Category: Data Science
English level: fluent
Years of experience: 3

Created:
- "Next Product to Buy" ML model.
- "Churn" ML model.
- Risk Machine Learning Models  using internal data  from Oracle and MongoDB databases
- PD Risk Scor Card E2E ML model ;
-Limit management strategy for customers based on ML outcome;
-API in Python for E2E ML implementation;
-E2E live dashboards in Qliksense for monitoring;
-Scraping data from different sites to help of conduction of Market Risk research.
-Resarch paper on "Traffic Sign Classification with Deep Neural Network" with 603 reads and 14 recommendations in ML
-Validation of ML models in Banking
1. Junior SQL Developer 
•	Responsibility:
Participated in the creation of a database for the Smart University system:
-Designing of database objects: tables, users, their connections, foreign keys, primary keys, etc.
-Checking whether ready design follows Relational Database Management System (RDMS) requirements for example     1NF,2NF,3NF, BC NF
-Creating data with Mysql, feeding data to tables, and monitoring the process flow
-Querying data for analysis, creating statistical reports for management about students' grades, exam points, etc with Tableau.

2.Data analyst at CRM
•	Responsibility: 
Designing campaigns, querying transactional, demographic, and other data from Data Warehouse using Transactional and Oracle SQL, or sometimes querying mobile applications’ log data from Google BigQeury(NoSql) to perform campaign design, creating statistical dynamic reports for pre/post analysis of the campaign, conducting A/B test, creating Machine Learning models like Churn model, Next Product to Buy with Python to find leads from customer population and prevent loss.

3.Model Risk Specialist 
•	Responsibilities:
-Conducting risk assessment of internal Machine Learning made by different divisions in bank for example Data Science team, CRM division, etc.
Model evaluation steps:
-Business requirement check (What is the problem in business)
- Which ML approach should be chosen. Which type of problem is this? Classification, regression, clustering, time series analysis or etc.
- Which algorithms should be chosen? Whether it is Linear regression, or K-means(traditional ML), or classification implementation with Neural Networks.
-Checking whether selected features(variable selection) are encompassing business problems well enough.
- Checking on Data preparation steps(EDA, missing data issues, data quality issues, train test split proportion ) and requirements according to chosen ML model's requirement 
- Assessing model's outcome based on applied algorithm (for example in classification precision, accuracy, recall, etc.