Principal Evaluation Scientist

Crisis Text Line, Inc.

1d•Remote

About The Position

About the Research & Impact Team The Research & Impact (R&I) team helps Crisis Text Line understand, demonstrate, and strengthen our impact—for people in crisis, our volunteer community, and the broader mental health field. We generate evidence-based insights, set standards for quality and ethical use of data and AI, and share learnings that influence practice, policy, and systems change. Our work is grounded in a unique, de-identified dataset of more than 11 million crisis conversations, analyzed using AI, machine learning (ML), and natural language processing (NLP). We use these insights to improve services, reduce inequities in mental health care, and advance well-being at scale. The Role As Principal Evaluation Scientist, you will lead rigorous evaluation and quality assessment efforts that demonstrate and deepen Crisis Text Line’s impact. Reporting to the VP of Impact Evaluation & Storytelling, you will serve as a senior individual contributor and methodological leader. You will design and guide complex, mixed-methods evaluations; translate findings into actionable insights; and partner closely with teams across research, clinical operations, product, and global affiliates. Your work will directly inform program improvement, strategic decision-making, funding efforts, and field-wide learning. This role is open to global applicants. Crisis Text Line is a fast-moving, growing organization with a big vision and a critical mission. To serve our mission and vision, each role must be agile and flexible to meet the needs of the business, aligning their work with priorities as defined by their manager and team leaders.

Requirements

8+ years of experience in evaluation, applied research, measurement, or social impact assessment
Deep expertise in mixed-methods evaluation, including quantitative and qualitative approaches
Experience with evaluation approaches such as impact evaluation, developmental evaluation, systems-oriented evaluation, participatory or community-based evaluation, and strategic learning
Strong methodological judgment, including advising on study design, sampling, and analytic strategies
Experience using or partnering on AI/ML-enabled approaches to support evaluation or analysis
Exceptional writing skills, with the ability to translate complex findings for diverse audiences
Proven ability to work cross-functionally in fast-paced, complex environments
Experience producing evaluation or research outputs for programmatic, policy, public, or philanthropic audiences
Reliable High-Speed Internet Required: Must have a stable high-speed internet connection to support seamless remote collaboration, virtual meetings, online job tasks, etc.

Nice To Haves

Bilingual in Spanish and English

Responsibilities

Lead rigorous mixed-methods evaluations, from study design through analysis and dissemination
Serve as a methodological partner to ensure evaluations are equitable, decision-ready, and actionable
Evaluate service quality, volunteer experience, texter outcomes, and systems-level change
Deliver rapid evaluations to inform new initiatives and evolving programs
Translate complex findings into clear insights that drive learning and strategy
Partner with Data Storytelling and AI/NLP research teams to produce insight products and reports
Create clear, compelling written materials and visuals grounded in strong methodology
Contextualize findings within mental health trends, stakeholder priorities, and CTL’s mission
Contribute evaluation frameworks and evidence-based narratives to funding proposals
Develop evaluation-driven materials for grant reporting and donor communications
Produce polished, audience-ready summaries, charts, and case examples
Develop evaluation methods and metrics for use across global partners and affiliates
Support shared learning with researchers and practitioners across the field
Apply emerging best practices in evaluation and systems change to strengthen impact
Help translate insights into action across the organization