A Data Analyst is a professional who is responsible for collecting, processing, analyzing, and interpreting complex data sets to provide actionable insights that drive business decisions.This role typically requires a combination of technical skills, domain knowledge, and strong communication abilities. Candidate is required to work on the following activities:

1. 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)

2. Identify any notable insights or trends discovered during the analysis and communicate them to the Lead Analyst.

3. Track the survey daily and provide updates to the project team.

4. Analyze the data and generate all possible insights from complex datasets.

5. Conduct a Quantitative data analysis and build a Dashboard either Power BI or Excel.

Here are the common requirements for a Data Analyst role, including a focus on databases: Educational Background:

- 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.

Technical Skills:

- 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 explicit 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 Handling:

- 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.

Problem-Solving Skills:

- 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.

Communication Skills:

- 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.

Collaboration:

- Teamwork: Ability to work collaboratively with cross-functional teams, including data engineers, business analysts, and stakeholders.

Tools and Technologies:

- 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.

- Hands-on experience with AI tools, especially Generative AI (e.g., GPT-5, Copilot, ChatGPT).

- Ability to leverage GPT for tasks such as automating data documentation and reporting, generating code snippets or SQL queries, enhancing data quality checks and anomaly detection, and summarizing large datasets or reports.

Additional Skills (depending on the role and industry):

- Familiarity with statistical concepts and techniques for data analysis.

- Basic knowledge of database querying languages (SQL).

- Experience with version control systems (e.g., Git).

- Basic understanding of machine learning concepts.

Experience:

- 2-3 years of relevant experience in data analysis or related roles, showcasing your ability to work with real-world data and generate insights.

📌 Research Data - Analyst / Sr Analyst (Bengaluru)
🏢 TPI Advisory Services
📍 Bengaluru