Economic Data Analyst Resume

SUMMARY

Professional experience as a Business Analyst with a strong understanding of Data Analysis; Database Management System (SQL); Big data & Artificial Intelligence; Data Mining & Predictive Analytics; Google Analytics; Data Processing in Python; Operations Analytics.
Experience in analytics capabilities of one or more public cloud platforms (AWS, Google, Azure).
Knowledge of programming languages like SAS, VBA, Query languages, etc.
Technical expertise in Data Integration, Data profiling, Data Cleaning.
Extensive experience in Text Analytics, developing different Statistical Machine Learning, Data Mining solutions to various business problems and generating data visualizations using Python and R.
Expertise in data analysis and visualization tools like MS Excel/Access, Tableau, data querying languages (e.g. SQL)
Have good knowledge in employing R & Python programming for data cleaning, data visualization, risk analysis and predictive analytics.
Experience with SQL Server and Excel.
Good knowledge and understanding of data administration.
Have practical understanding of statistical modeling and machine learning techniques with keen interests in applying these techniques to predictive analytics word.
Experience with a variety of relevant data manipulation languages (Python, R).

TECHNICAL SKILLS

Programming Languages: Python, R, SQL, VBA, SAS

Databases: NoSQL, MongoDB, Hadoop, Spark, Hive

BI Tools: Tableau, Google Analytics, Microsoft Excel, Power BI

Cloud Platforms: AWS, Google Cloud, BigQuery, AzureOther Tools Machine Learning, ETL, Salesforce, SAS, A/B Testing, R Shiny, SharePoint, MATLAB, JIRA

PROFESSIONAL EXPERIENCE

Confidential

Economic Data Analyst

Responsibilities:

Generated the development quantitative models, methodologies, and business processes and must be conversant in using statistical packages such as SAS, R, Python, Stata, and process mining programs.
Collected and harmonized data from socio - economic sources, Confidential enterprise systems, and foreign assistance data from a wide and present use cases, ERD, functional, non- functional documents in visually intuitive formats.
Support the development quantitative models, methodologies, and business processes and must be conversant in using statistical packages such as SAS, R, Python, Stata, MATLAB and related programs.

Environment: Microsoft Excel, SharePoint, Tableau, Python, SQL, R Shiny, SAS, MATLAB

Confidential, Washington, DC

Data Analyst

Responsibilities:

Designed and implemented industrial analysis strategy; Extracted, Transferred and loaded (ETL) data and analyzed findings to gain industrial insights; Generated data visualizations and reported action plans by SAS, Python, and Tableau.
Developed data visualizations to illustrate the survey dataset of the IFC DeCODE pilot, including preparing R Shiny, PowerPoint slides, creating Excel v-lookup and pivot tables and Tableau Dashboard.
Implemented A/B Testing and UAT to achieve operational objectives by utilizing the process mining of Celonis, managing analytics process and recommendations to strategic plans and reviews.

Environment: Python, ETL, Tableau, Celonis, A/B Testing, UAT, R Shiny, Microsoft Excel, SAS.

Confidential

Data Analyst

Responsibilities:

Applied SQL, ETL, Python-related machine learning models (Logistic Regression, Gradient Boosting, Neural Network, etc.) to perform valuation analyses and financial modelling, to provide financial due diligence reports,3-year financial forecast analysis, write investment proposal reports, BRD, FSD and optimizing business plans.
Implemented A/B Testing to create fund portfolios and carried out sensitivity analysis to value the target company.
Provided diversified portfolios with maximized return based on 80+ QDII funds including Stock Fund, Hybrid Fund, considering their forecasting performances and risk assessments based on Morningstar Rating.

Environment: SQL, Python, ETL, Logistic Regression, A/B Testing, Machine Learning, SAS

Confidential

Data Analyst

Responsibilities:

Developed customized financial plans for the clients based on different property scale, objective and risk tolerance.
Advised clients to financial products including property insurance, life insurance consistent with wealth preservation.
Managed Data quality & integrity using skills in Data Warehousing, Databases & ETL.
Created Custom dashboards product category wise by Data Visualization libraries used in R and Python.
Prepared the model data and built various Supervised and Unsupervised Learning Models using R and Python.
Established multiple valuation models, carried out benchmarking and ETL analysis to value the target company, and calculated the available credit limit of different financing operating methods by R Shiny.
Performed data cleaning, exploration implementing Python and R.

Environment: ETL, R Shiny, Python, SAS

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