#96260 Data Analyst (МакКинси) Hot vacancy

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Olga Arkhypova

Job Responsibilities 
Interpreting and translating business problems into analytical solutions for optimization and development of Workforce Planning model;
Involvement in the entire project life cycle including data extraction, data cleaning, statistical modeling, and data visualization with large data sets of structured and unstructured data.
Creating algorithms & models to identify drivers of growth, losses and profitability for initiatives to support business decisions;
Designing the system data flow to DWH with support of Data Architects;
Analytical support for project initiative effects estimation & creation of decision support reports;
Communication with key stakeholders (HR, Finance, Operational & Commercial Departments) to design & implement Workforce Planning initiatives;
Keeping close contact with regional management and Continuous Transformation team.
Bachelor’s Degree in Mathematics / Quantitative Economics / Econometrics / Statistics / Computer Sciences / Finance;
At least 1 year working experience on Data & Analytics;
        Strong mathematical background in Linear algebra, Probability, Statistics& Optimization Techniques;
Experience in handling Structured and Unstructured data, writing advanced SQL queries, Data Wrangling, Data Visualization, Data Acquisition & Predictive modeling;
Willingness to learn, professionalism and dedication, able to work under pressure;
Must Have
Python (at least numpy, pandas, sklearn, pulp);
SQL (Window & Analytic functions, Nested Queries);
Advanced Excel (Pivot Tables & VBA);
Skills in Advanced Regression Modeling, Probabilistic Graphical Models, Multivariate Analysis, Time Series Analysis and application of Statistical Concepts (Hypothesis testing, A/B tests, Factor analysis / PCA).
Experience of modelling non-continuous events, implementation of Linear Optimization Methods (Mixed-Integer Linear Programming);
Knowledge of Reporting and Business Intelligence Software (Power BI, Tableau, Qlik, Cognos Analytics);
Machine Learning techniques (Decision Trees, Random Forest, SVM, Bayesian, XG Boost, K-Nearest Neighbors).


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