Position Overview: The Data Analyst plays a crucial role in collecting, analyzing, and interpreting data to provide insights and support decision-making across the organization. This position involves extracting and transforming data, conducting statistical analysis, and presenting findings in a clear and actionable manner to inform strategic planning and program evaluation efforts.
Key Responsibilities:
- Collect, clean, and preprocess data from various sources, including databases, spreadsheets, surveys, and external APIs.
- Perform exploratory data analysis to identify trends, patterns, and correlations, and uncover insights that drive informed decision-making.
- Develop and implement data models, algorithms, and predictive analytics to forecast trends, predict outcomes, and optimize performance.
- Create data visualizations.
- Collaborate with team members to define key performance indicators (KPIs), metrics, and benchmarks to track progress and measure the impact of initiatives.
- Conduct ad hoc analyses and respond to data requests from internal teams, partners, and stakeholders in a timely and accurate manner.
- Stay current with emerging trends, best practices, and new technologies in data analytics, machine learning, and data visualization, and incorporate them into our analytical approach as appropriate.
- Contribute to the development and enhancement of data infrastructure, tools, and processes to streamline data management and analysis workflows.
- Support capacity building and knowledge sharing activities, such as training sessions, workshops, and documentation, to empower colleagues to use data effectively in their work.
Qualifications:
- Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, Economics, or a related field; advanced degree preferred.
- Proven experience (2-5 years) in data analysis, business intelligence, or a related role, preferably in a non-profit, research, or social impact context.
- Proficiency in data manipulation and analysis using programming languages such as Python, R, SQL, or similar tools and libraries.
- Experience with data visualization and dashboarding tools (e.g., Tableau, Power BI, matplotlib, seaborn) to create compelling and informative visualizations.
- Strong analytical skills, with the ability to apply statistical methods, machine learning techniques, and quantitative analysis to extract insights from complex datasets.
- Familiarity with data cleaning, preprocessing, and transformation techniques to ensure data quality and integrity.
- Excellent communication skills, including the ability to convey technical concepts and findings to non-technical audiences in a clear and accessible manner.
- Detail-oriented and organized, with the ability to manage multiple projects, priorities, and deadlines effectively.
- Collaborative team player with a proactive attitude and willingness to learn and adapt in a fast-paced environment.
