Data Scientist

Responsibilities

Data Analytics & Strategic Insights

Collaborate with the Data Team and Program Directors to develop an institutional data practice where we ask powerful questions, collect relevant data, generate insights, and take action:

• Conduct analyses to inform RLD Foundation’s grantmaking strategy, help answer research questions, and address organizational learning priorities, including ecosystem mapping, trend analysis, and testing theories of change.
• Apply machine learning, natural language processing, geospatial analysis, and other advanced modeling techniques to extract insight from structured and unstructured data.
• Creatively source and curate relevant datasets from public, private, academic, and alternative sources relevant to RLD Foundation’s work and to fill sector data gaps.
• Produce data briefs and white papers that share findings transparently with grantees and the broader sector.
• Conduct exploratory data analysis across RLD Foundation’s internal and partner datasets to identify trends, gaps, and opportunities.
• Design clear, compelling data visualizations and narratives to communicate complex findings to program staff, foundation leadership, board, and external stakeholders.
• Translate complex technical concepts for diverse audiences with varying levels of data familiarity, ensuring insights are accessible and actionable.

Data Infrastructure, Systems, and Tools

Lead the development and maintenance of modern and scalable data infrastructure to undergird RLD Foundation’s data strategy:

• Architect and maintain RLD Foundation’s cloud-based data infrastructure, ensuring performance, scalability, and security.
• Develop and automate ETL/ELT pipelines to integrate data from internal systems (grants management, CRM, program data) and external sources (public APIs, data partners, research institutions, government).
• Support the development of analytical tools and web-based platforms that enable staff, board, and ecosystem partners to access real-time insights.
• Support RLD Foundation staff to use relevant data systems, tools, and processes.
• Implement rigorous data quality, validation, and governance protocols.
• Leverage AI and automation tools to streamline data ingestion, cleaning, and analysis.
• Develop data dictionaries, and document all code and data processing workflows.

Data Strategy

• Partner with the Director of Data Strategy & Insights, Program Directors, Grants Manager, and Research Analysts to develop data collection plans supporting strategic learning, monitoring, and evaluation.
• Contribute to the design and tracking of indicators and metrics that can help RLD Foundation better understand baselines, progress, and trends in its focus issue areas.
• Collaborate across the organization to ensure all data collection systems and tools are thoughtfully designed to capture information that aligns with organizational needs and learning questions.
• Contribute to a culture of iterative learning around data use.

Grantee & Field Capacity Building

Contribute to leading RLD Foundation’s grantee-centered approach to data capacity building, helping nonprofits use data to strengthen their own work, not just report to funders:

• Work with grantees to understand their data aspirations, current pain points, and where RLD Foundation support could help them take the next step in their data maturity, applying participatory approaches that center grantee voice and needs.
• Provide hands-on technical assistance to grantees, when applicable.
• Identify and manage data capacity building partners that can augment and complement RLD Foundation’s direct technical assistance.
• Create public-facing data tools, open data portals, and resources for grantees and the broader field.
• Contribute analysis and expertise to collaborative initiatives that enable grantees, peer funders, and others to learn together from data, facilitating shared meaning-making and collective reflection.
• Advise on potential grantmaking efforts to fill sector data gaps.

Skillset

• 5+ years of experience in data science, analytics, data journalism, or related role.
• Mission alignment: Demonstrated commitment to social impact and the values of the Foundation.
• Programming expertise: Advanced proficiency in Python and/or R for data analysis, modeling, and automation.
• Data Analytics and Modeling: Strong foundation in statistical methods, predictive modeling, and machine learning.
• Data infrastructure: Experience with cloud databases (e.g., AzureSQL, BigQuery, Snowflake), data warehouse architecture, ETL/ELT pipelines, and connecting to APIs.
• Spatial analysis: Experience with GIS tools and spatial data analysis (e.g., QGIS, or spatial Python/R libraries).
• Data visualization: Ability to create clear, compelling visualizations and communicate insights to non-technical audiences.
• Communication: Ability to explain complex concepts clearly and patiently, adapting to match individual learning needs.
• Technical versatility: Comfortable learning new tools, technologies, and working across the data stack.
• Version control: Proficiency with Git/GitHub for collaboration and code management.