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Research Data - Analyst / Sr Analyst
Information Services Group
USA
Full-time
Entry
Onsite
Discovered 1 weeks ago
Free
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- Conduct a comprehensive analysis of the responses received from providers, examining each question, and proposing any necessary question deletions. (no. of questions attempted by the service providers, most answered/unanswered questions)
- Identify any notable insights or trends discovered during the analysis and communicate them to the Lead Analyst.
- Track the survey daily and provide updates to the project team.
- Analyze the data and generate all possible insights from complex datasets.
- Conduct a Quantitative data analysis and build a Dashboard either Power BI or Excel.
- A bachelor's degree in a relevant field such as Computer Science, Statistics, Mathematics, Economics, Business, or a related discipline. Master's degree or higher is preferred.
- Data Analysis with Python: Proficiency in using Python for data manipulation, cleaning, and analysis. Familiarity with libraries like pandas, numpy, and scipy for data handling.
- Excel Proficiency: Strong skills in Excel, including functions, formulas, pivot tables, data visualization, and basic macros.
- Data Visualization: Ability to create clear and informative visualizations using Python libraries like Matplotlib and Seaborn for enhanced data communication.
- SQL: Basic knowledge of SQL for querying databases and extracting relevant data.
- Data Cleaning and Transformation: Ability to clean and preprocess raw data, handle missing values, and transform data into a usable format.
- Data Extraction: Proficiency in extracting data from various sources, including databases, APIs, CSV files, and Excel spreadsheets.
- Analytical Thinking: Capacity to analyze complex data sets, identify patterns, and draw meaningful insights to address business questions.
- Critical Thinking: Ability to approach problems logically, formulate hypotheses, and develop data-driven solutions.
- Data Reporting: Ability to present findings effectively through reports and visualizations that convey insights to both technical and non-technical stakeholders.
- Data Storytelling: Skill in translating data findings into compelling narratives that drive decision-making.
- Teamwork: Ability to work collaboratively with cross-functional teams, including data engineers, business analysts, and stakeholders.
- Proficiency in using Python for data analysis (pandas, numpy), data visualization (Matplotlib, Seaborn), and potentially Jupyter notebooks.
- Strong Excel skills, including functions, formulas, pivot tables, charts, and data manipulation.
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