Job Description
We are seeking a highly analytical and detail-oriented Part-Time Data Analyst to join our dynamic team in Toledo, Ohio. In this role, you will play a pivotal part in transforming raw data into actionable business intelligence, helping stakeholders make informed decisions. If you are passionate about uncovering trends and possess a knack for statistical modeling, we want to hear from you.
At Toledo Regional Analytics, we value flexibility, precision, and innovation. As a part-time team member, you will enjoy a hybrid work environment that accommodates your schedule while delivering high-impact results. Join us in driving the future of data-driven decision-making in the Heart of Ohio.
Responsibilities
- Data Collection & Cleaning: Gather, clean, and validate data from various sources to ensure accuracy and integrity for analysis.
- Statistical Analysis: Perform complex statistical analysis and modeling to identify patterns, trends, and correlations within datasets.
- Reporting & Visualization: Create clear, concise, and visually appealing dashboards and reports using tools like Power BI, Tableau, or Excel.
- Stakeholder Collaboration: Work closely with cross-functional teams to understand their data needs and translate business questions into analytical solutions.
- Database Management: Assist in maintaining and optimizing SQL databases, writing queries to retrieve specific data sets efficiently.
- Process Improvement: Identify opportunities for process improvement and efficiency gains based on data insights.
Qualifications
- Education: Bachelor’s degree in Mathematics, Statistics, Computer Science, Information Systems, or a related field is preferred.
- Experience: 1-3 years of experience in data analysis, preferably in a part-time or freelance capacity.
- Technical Skills: Proficiency in SQL for data querying and strong knowledge of statistical software (R, Python, or SAS).
- Visualization: Demonstrated experience creating visualizations with Power BI, Tableau, or Excel (Pivot Tables, VLOOKUP).
- Communication: Excellent verbal and written communication skills with the ability to explain complex data concepts to non-technical audiences.
- Attention to Detail: A keen eye for detail and the ability to detect errors or inconsistencies in large datasets.