Senior Data Scientist & AI Engineer

Responsibilities

This role’s primary focus is building data infrastructure and conducting analytics, with significant investment in developing AI-enabled knowledge systems. You’ll also contribute to broader data strategy conversations and selective grantee capacity building. Day-to-day work spans the following areas:

Data Infrastructure, Systems, and Tools

Lead the development and maintenance of modern and scalable data infrastructure to undergird RLD Foundation’s data strategy and AI-enabled knowledge platform:

• Architect and maintain RLD Foundation’s cloud-based data infrastructure, ensuring performance, scalability, and security
• Develop and automate ingestion and transformation pipelines to build an integrated warehouse of 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
• Implement rigorous data quality, validation, and governance protocols
• Develop data dictionaries, and document all code and data processing workflows

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:

• Apply statistical, machine learning, and geospatial techniques for applied analysis, with advanced NLP and LLM-based methods primarily used within the AI-enabled knowledge platform
• 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
• Conduct exploratory data analysis across RLD Foundation’s internal and partner datasets to identify trends, gaps, and opportunities
• Advise on key data points for data briefs and white papers that share insights transparently with grantees and the broader sector

AI-Enabled Knowledge Platform

Building on RLD Foundation’s core data infrastructure outlined above, this role will:

• Lead the design and development of an AI-enabled knowledge layer, in collaboration with external partners as appropriate, that integrates structured data (grants, finances, open data, acquired private data) and unstructured data (reports, research, meeting notes, transcripts), into a unified, query-able system
• Build retrieval-augmented generation (RAG) pipelines that allow staff to ask complex strategy questions and receive synthesized, source-grounded answers (i.e. “What strategies have been most effective in creating family-sized affordable housing in Chicago, and where do gaps remain?”)
• Implement metadata, embeddings, and document indexing to support semantic search, cross-dataset linking, and transparent sourcing
• Partner with program teams to translate learning and strategy questions into AI-supported workflows and analytical products
• Ensure responsible AI practices, including data governance, bias awareness, privacy protection, and explainability of outputs

Data Strategy

• Partner with the Director of Data Strategy & Insights, Program Directors, Grants Manager, and Research Analysts to identify datasets to acquire and analyses to conduct that support strategic learning, monitoring, and evaluation priorities
• 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 RLD Foundation’s grantee-centered approach to data capacity building, supporting nonprofits use data to strengthen their own work. This work will be selective and episodic, not a primary day-to-day responsibility:

• Work with select grantees to understand their data aspirations, current pain points, and where RLD Foundation support could help them build data capacity
• Partner with grantees and capacity-building vendors to shape and support data initiatives; provide hands-on support selectively when it creates high leverage for learning or infrastructure
• Contribute analysis and expertise to collaborative initiatives that enable grantees, peer funders, and others to learn together from data
• Create public-facing data tools and resources for grantees and the broader field

Skillset

Required Qualifications

• Typically 7+ years of experience in data science, engineering, analytics, AI, or related role
• Mission alignment: Demonstrated commitment to social impact and the values of the Foundation
• Data infrastructure: Experience with cloud data platforms (e.g., Snowflake, Azure, BigQuery, AWS), data warehouse architecture, and building automated ETL/ELT pipelines (e.g. dbt, Airflow)
• Programming expertise: Advanced proficiency in SQL, and Python and/or R for data analysis, modeling, and automation
• Data analytics and modeling: Strong foundation in statistical methods, predictive modeling, and machine learning; experience applying these methods to real-world problems
• Practical experience with modern AI/ML concepts (LLMs, embeddings, transformers); experience applying AI/ML methods to real problems.
• 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

Preferred Qualifications

• 8-10+ years of experience and/or an advanced degree in data science, engineering, analytics, AI, or related role
• Experience in civic technology, nonprofit organizations, philanthropy, or government
• Exposure to working with vector databases, embeddings, or modern document stores
• Experience integrating structured and unstructured data for analysis
• Familiarity with responsible AI, model evaluation, and human-in-the-loop workflows
• Experience building web-based data products or interactive applications (dashboards, data explorers, mapping tools)
• Experience providing technical assistance or data capacity support to external partners
• Experience strengthening data ecosystems or collaborative data infrastructure across multiple organizations
• Knowledge of data governance, privacy, and ethics frameworks

Personal Qualifications

• Demonstrated ability to work as part of a team and with people who hold diverse perspectives
• Highly developed emotional intelligence and demonstrated ability to use interpersonal skills and political acumen in respectful and collaborative ways
• Flexibility, commitment to teamwork, curiosity, and a sense of humor
• Capacity to work amicably in a small office with high volume of work, as well as a deep sense of responsibility and accountability
• Ability to make decisions, justify recommendations and be responsible and clear with stakeholders
• A record of recognizing and acting on opportunities to continuously improve
• High degree of professional ethics and integrity
• Ability to work autonomously