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Data Analyst Research Assistant Resume

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TX

SUMMARY

Concept - to-execution, innovative, detail-oriented, equipped with in-depth knowledge of applied data analysis, data visualization, mathematical modeling, machine learning and time series analysis. Adept at creating cutting-edge solutions to streamline procedures and achieve client utmost satisfaction. Armed with exceptional interpersonal skills in building rapport with colleagues and diverse professionals.

PROFESSIONAL EXPERIENCE

Confidential,TX

Data Analyst Research Assistant

Responsibilities:
  • Drive efforts in creating mathematical modeling of spread of biodegradable anti-pathogen
  • Simulation-based estimation of desired parameters required to run the test in the laboratory
  • Texas inpatient project:
  • Take charge of data visualization
  • Quantitative data analysis such as building prediction model for patients’ length of stay in hospital for each facility based on different categories of disease

Confidential

Data Analyst

Responsibilities:
  • Got involved in data analysis process of clinical trials, and medical and non-medical academic studies by:
  • Writing statistical analysis plan
  • Providing general study randomization
  • Sample size estimation by conducting methods such as: power analysis and the coefficient of variation
  • Interpretation of data by data visualization, tables and figures.
  • Developing statistical models based on machine learning algorithms (univariate and multivariate regression, classification methods) to identify the trend, effective factors, and to build and run prediction model
  • Maintained active involvement in a special case study conducted by a neurologist for Alzheimer, Parkinson’s, diseases to examine the geographical condition and job environment effects on infection and progress of the disease

Confidential

Data Analyst

Responsibilities:
  • Generated various daily reports for used and waste raw materials, number of manufactured final and high-quality products, as well as low-quality and damaged goods, including new orders amount for each types
  • Managed production schedules in accordance with limited number of machines to ensure timely delivery
  • Streamlined production procedures by classifying various types of machine failures, as well as the happening time and shift to analyze corresponding data and discover important reason and generate solution
  • Mitigated any caused of delay by lacking of raw materials through effective forecasting

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