Objectives of this role

The Data Analyst will be responsible for collecting, analyzing, and interpreting data to provide meaningful insights that drive strategic decision-making and support business objectives. They will utilize their analytical skills to identify patterns, trends, and correlations within datasets and translate them into actionable recommendations. The objective of this role is to leverage data to help improve operational efficiency, inform business strategies, and optimize performance across various departments within the organization. The Data Analyst will play a critical role in enabling data-driven decision-making and ensuring data integrity and accuracy in all analytical processes.

 

Responsibilities

Collect, analyze, and interpret data to provide insights and support decision-making processes. Identify trends, patterns, and anomalies in datasets to uncover key insights and drive business improvements. Develop and maintain data models and tools for data analysis, ensuring accuracy and reliability. Collaborate with cross-functional teams to gather requirements and define data analysis objectives. Perform data cleansing, validation, and transformation to ensure data quality and integrity. Conduct statistical analysis, data mining, and predictive modeling to identify correlations and make forecasts. Create reports, dashboards, and visualizations to present data findings and effectively communicate insights to stakeholders. Identify areas for process improvement and make recommendations for optimizing data collection, analysis, and reporting. Stay up to date with industry trends, data science methodologies, and analytical tools to continuously enhance analytical capabilities. Assist in developing and implementing data-related projects and initiatives. Bachelor's degree in a quantitative field such as Mathematics, Statistics, Economics, or Computer Science. A master's degree is preferred. Proven experience as a data analyst or in a similar analytical role. Proficiency in SQL for querying and analyzing large datasets. Expert knowledge of Excel, including advanced functions and formulas. Strong programming skills in languages like Python or R for data manipulation and analysis. Familiarity with data visualization tools such as Tableau, Power BI, or QlikView. Experience with statistical analysis, regression models, and predictive modeling techniques. Solid understanding of data warehousing concepts and data structure principles. Ability to effectively communicate complex data findings to both technical and non-technical stakeholders. Strong problem-solving skills and attention to detail. Experience with data cleansing, data quality assessment, and data transformation techniques. Knowledge of database management systems and data querying optimization techniques.