Data Analyst

Requirements:

• Bachelor's degree in a quantitative field such as Computer Science, Statistics, Mathematics, or related disciplines.
• Strong analytical and problem-solving skills with a keen attention to detail.
• Proficiency in data analysis and visualization tools such as Python, R, SQL, Excel, Tableau, or similar tools.
• Solid understanding of statistical concepts and techniques.
• Familiarity with data manipulation, data cleaning, and data preprocessing techniques.
• Excellent communication skills, both verbal and written, with the ability to present complex data in a clear and concise manner.
• Strong organizational skills and the ability to manage multiple tasks and projects simultaneously.
• Eagerness to learn and adapt to new technologies and tools in the field of data analysis.
• Ability to work collaboratively in a team environment as well as independently with minimal supervision.

Preferred qualifications:

• Experience with data visualization and dashboarding tools such as Tableau, Power BI, or QlikView.
• Knowledge of programming languages such as Python or R for data analysis and machine learning.
• Familiarity with basic machine learning concepts and techniques.
• Experience with data extraction, transformation, and loading (ETL) processes.
• Understanding of database management systems and SQL queries.

Note: As this is a fresher-level position, relevant internships, coursework projects, or personal projects showcasing data analysis skills will be considered valuable assets.

Responsibilities:

• Collect, clean, and analyze large datasets to identify patterns, trends, and insights.
• Collaborate with the Data Science team to develop and implement data models and algorithms.
• Perform statistical analysis and data mining techniques to uncover key insights and provide actionable recommendations.
• Prepare and present data-driven reports, visualizations, and dashboards to communicate findings to various stakeholders.
• Assist in designing, implementing, and maintaining data collection systems and databases.
• Conduct data quality assessments to ensure data accuracy, completeness, and integrity.
• Support ad-hoc data analysis requests from different departments within the organization.
• Stay up-to-date with industry trends, best practices, and emerging technologies in data analysis and data visualization.