Position: Data Scientist
Category: Data Science
English level: fluent
Years of experience: 9

Data Scientist with diverse work experience in various fields of Advanced Analytics, Financial Risk Management and Business Consulting. Highly organized, motivated, and diligent professional with:

•	3+ years’ experience in developing and delivering data-driven insights and recommendations by leveraging traditional and state-of-the-art machine learning and statistical methods in 10+ projects.
•	Professional consulting experience, technical expertise, and business acumen to translate business requirements and objectives into scalable, highly resilient, and successful solutions. Savvy at understanding data and translating it into meaningful insights with transferrable problem-solving skills and knowledge acquired from previous experience.
•	2+ years’ experience in building, managing, coaching, and growing a Data Scientists team of 3–6 people by improving the team’s technical excellence, conducting performance reviews, establishing clear and strategic KPIs, and facilitating a positive work environment that highly encourages creative input and constructive criticism.
•	Expertise in managing the Data Science Life Cycle and building end-to-end data pipelines for Machine Learning models including Business and Data Understanding, Data Acquisition, Data Preparation, Data Manipulation, Feature Engineering, Modeling, Testing and Validation, Visualization and Reporting the insights.
•	Demonstrated management, multi-tasking skills and ability to learn quickly under minimal supervision. Solid background in timely resolution and proven flexibility in meeting responsibilities.
•	Proficiency in leveraging data from multiple sources to create visually powerful and actionable interactive reports and dashboards as a part of Data Storytelling. Collaborative and excellent at communicating complex concepts and results in simple terms. Hands-on experience in communicating business insights by dashboarding using Power BI, R and Python.
•	Deep understanding and proficiency in Feature Engineering, Statistical Modelling, Predictive Analytics, and Machine Learning, including Regression, Classification, Clustering, Dimensionality Reduction and Ensemble methods.
•	Proficient in Python, R and SQL coding with experience in Object-Oriented Programming and working in Microsoft Excel and Microsoft Power BI in an advanced level.
Professional Consultant and Data Scientist at Ernst & Young (EY) company:

•	Consistently met the diverse needs of clients from top banks and companies in Azerbaijan, UAE, Russia, Kazakhstan, and Uzbekistan for 20+ evolving projects in fast-paced, high-pressure environments.
•	Built predictive credit scoring models in Python by using Logistic Regression and Support Vector Machine algorithms to accurately maximize risk-adjusted returns.
•	Utilized Stochastic models, Markov chains, Monte Carlo simulation, etc. in assessment of default risk in over 10 projects.
•	Qualitatively and quantitatively validated retail segment scoring model with 20k variables, approximately 1 million observations of disparate data, and 800 million USD under management using Univariate Analysis, Principal Component Analysis (PCA), Kolmogorov–Smirnov statistics, Univariate and Multivariate Logistic Regressions, Gini scores, AUC, Population Stability Index (PSI), Maximum Concentration Index, Herfindahl–Hirschman Index and other statistical and machine learning methods.
•	Developed an automated machine learning tool for macro-economic predictions in Python to find a correct combination of variables by assessing different parameters, metrics, and assumptions. The tool has reduced calculation time from one week to an hour.
•	Built a web app using R Shiny framework for automatic end-to-end calculation of expected credit loss by reducing calculation time from one week to an hour with 500 million USD under management.
•	Modeled default and loss-given default probabilities by collecting five years of historical monthly utility data for 3 million subscribers and transforming it to calculate overdue days per subscriber using SQL and R.
•	Wrote an automated web scraping tool using Excel VBA to extract, transform, and structure public statistical and financial data from over 1k URLs without manual interference.
•	Reviewed, commented, and corrected SQL codes written by audit clients as part of 10+ audit support procedures.
•	Communicated data-driven results and provided recommendations to assist 40+ audit and business consulting projects.
•	“Employee of the Month” for an exceptional work.

Language skills: English - Fluent, Azerbaijani - Native, Russian - Fluent, Turkish - Fluent