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Think of Us

Data-driven foster care reform lab

Product Lead

Product ManagementConfirmed live in the last 24 hours
Full-timeWashington, DC, USA$150k - $180k /yr

About the Job

Requirements

  • Eight or more years of product-management experience, including at least three to four years as a lead or highly senior product manager on software products.
  • A demonstrated track record of shipping and improving software products rather than primarily managing programs, contracts, projects, or requirements documents.
  • Experience owning meaningful product decisions, including roadmap prioritization, feature scope, release readiness, and cross-functional tradeoffs.
  • Experience defining success, instrumenting products, interpreting evidence, and iterating based on real-world performance.
  • Technical fluency sufficient to understand architecture tradeoffs, data flows, application programming interfaces, privacy and security implications, artificial intelligence limitations, and technical specifications.
  • Experience working closely with Design and Engineering from early problem framing through production release.
  • Experience with artificial-intelligence-powered or probabilistic products where quality is difficult to define, trust must be earned, and human review is part of the product.
  • Ability to distinguish among model quality, product quality, user experience, operational performance, and demonstrated impact.
  • Experience navigating complex, multi-stakeholder environments involving technical, programmatic, operational, and external partners.
  • Ability to translate ambitious and evolving executive ideas into structured product opportunities and clear recommendations.
  • Experience working in an early-stage or resource-constrained environment where product practices had to be established rather than inherited.
  • Experience working with or alongside systems that affect young people and families, or a deep commitment to learning about these systems with humility, curiosity, and care.
  • Motivation to contribute to social impact and systems change while maintaining discipline about what technology can and cannot accomplish.
  • Take ownership in a lean environment, set priorities, and move work forward without waiting for detailed direction.
  • Engage confidently with active executive leadership without becoming passive or defensive.
  • Listen for the strategic intent beneath evolving ideas and separate the underlying opportunity from an assumed solution.
  • Distinguish brainstorming, recommendations, organizational direction, external commitments, and product decisions.
  • Challenge assumptions respectfully using user evidence, operational context, technical realities, data, and clear reasoning.
  • Make tradeoffs visible and state what must stop, move, or change when a new priority is introduced.
  • Protect focus without becoming rigid or using product process to resist legitimate strategic change.
  • Decide under uncertainty and know when a decision requires more evidence, broader consultation, or executive escalation.
  • Communicate clearly in writing and in person, including when explaining what is not on the roadmap and why.
  • Build alignment across Product, Design, Engineering, Data and AI, Program, Operations, Policy, and executive leadership.
  • Move quickly without confusing urgency with haste.
  • Use structure to create clarity, not bureaucracy.
  • Bring sound judgment and genuine care to decisions that affect people navigating stress, crisis, and institutional systems.
  • Take responsibility for decisions rather than allowing unclear consensus or endless consultation to substitute for ownership.

Responsibilities

  • Define and maintain the ConnectMe product roadmap within Think of Us’ organizational strategy and program goals.
  • Decide what gets built, in what order, why it matters, and what the team will not pursue.
  • Frame work in terms of outcomes, including the change in user experience, staff behavior, service quality, or operational performance the team is trying to create.
  • Prioritize across feature opportunities, user feedback, operational needs, defects, technical debt, research findings, and state requests.
  • Make tradeoffs among speed, scope, quality, learning, safety, and long-term sustainability visible.
  • Present the roadmap clearly to leadership, program teams, technology teams, and state partners.
  • Revisit roadmap decisions when meaningful new evidence or organizational direction emerges.
  • Write clear, well-scoped product and feature definitions that include the problem, hypothesis, intended outcome, success criteria, constraints, risks, and expected learning.
  • Work with the Lead Product & UX Designer to ensure the experience is sufficiently researched and designed before development begins.
  • Work with Engineering to understand feasibility, architecture, security, privacy, maintenance, and technical debt implications.
  • Identify scope creep, hidden complexity, unclear requirements, and unresolved assumptions before they become delivery problems.
  • Own acceptance criteria and product release decisions once required cross-functional reviews are complete.
  • Document major decisions and the tradeoffs behind them.
  • Determine where artificial intelligence genuinely helps and where human expertise, conventional software, operational changes, or no automation are more appropriate.
  • Define what work artificial intelligence may perform, what it may recommend, what requires human review, and what must remain human-led.
  • Partner with Design, Engineering, Programs, the Director of Data & AI, and the Senior Applied Data Scientist on artificial-intelligence-enabled workflows.
  • Define the intended user value, product boundaries, failure conditions, and quality expectations for artificial-intelligence features.
  • Partner with the data and artificial intelligence team on evaluation frameworks, datasets, quality thresholds, monitoring, and known limitations.
  • Make product tradeoffs based on model performance, user experience, operational impact, equity, risk, cost, and reliability.
  • Decide whether an artificial-intelligence capability is ready to release, should be limited, requires another iteration, or should be paused.
  • Escalate direct-to-user artificial intelligence, material safety concerns, or changes in organizational risk posture to executive leadership.
  • Define what success looks like for each major feature, including leading and lagging indicators and artificial-intelligence-specific quality measures where relevant.
  • Partner with the Senior Director, Data Strategy and program leaders to connect product measures to broader program and organizational outcomes.
  • Partner with Engineering and the data and artificial intelligence team to ensure features are appropriately instrumented.
  • Use product analytics, qualitative research, field observations, operational evidence, and artificial-intelligence evaluations to understand whether the product is working.
  • Decide what to expand, revise, simplify, limit, or stop based on real-world evidence.
  • Communicate clearly about what the evidence shows, what it does not show, and what the team still needs to learn.
  • Build a culture of shipping, measuring, learning, and improving rather than building to a fixed specification.
  • Coordinate with program leadership, state partners, Community Responders, young people, families, and caregivers to understand the problem space.
  • Work with Programs to determine whether a need is best addressed through product, operations, practice changes, training, policy, or another intervention.
  • Translate user needs, field signal, program requirements, and operational realities into product opportunities and decisions.
  • Distinguish between state-specific requests and capabilities that should become part of the shared product.
  • Evaluate customization requests based on strategic value, reuse, cost, and maintenance requirements.
  • Communicate clearly about what is and is not on the roadmap and why.
  • Avoid making commitments before product, technical, program, and capacity implications have been assessed.
  • Establish structured feedback mechanisms that do not rely only on the loudest or most available stakeholders.
  • Lead the cross-functional product team through clear priorities, decisions, and shared outcomes.
  • Own release planning, including what ships in each cycle, in what sequence, and with what dependencies.
  • Coordinate across Product, Design, Engineering, Data and AI, Program, and Implementation.
  • Ensure design, technical quality, security, data, program, implementation, and support readiness are considered before release.
  • Identify risks and dependencies early.
  • Coordinate quality assurance and launch readiness without personally owning every testing or implementation task.
  • Establish clear plans for pilots, phased releases, state deployments, monitoring, support, and rollback.
  • Ensure the team learns from production performance after release.
  • Establish lightweight product practices appropriate for a lean, mission-driven, artificial-intelligence-enabled organization.
  • Create clear standards for opportunity framing, discovery, prioritization, product briefs, roadmap communication, release readiness, measurement, and decision documentation.
  • Use artificial-intelligence tools to accelerate research synthesis, product analysis, prototyping, documentation, testing, and routine coordination while maintaining human judgment and data safeguards.
  • Help colleagues understand the difference among organizational strategy, program needs, product opportunities, features, and technical tasks.
  • Create enough structure to produce clarity and accountability without introducing unnecessary bureaucracy.
  • Help Think of Us develop the ability to evaluate and operate multiple products over time.

Desired Qualifications

  • Background in civic technology, social impact, healthcare, public benefits, human services, education, or another high-stakes environment.
  • Experience working with government or state partners while maintaining a coherent product strategy.
  • Familiarity with HIPAA, privacy, consent, accessibility, safeguarding, or other regulatory environments.
  • Experience designing or operating human-in-the-loop artificial-intelligence systems.
  • Experience with copilots, conversational products, artificial-intelligence agents, retrieval-augmented generation, or related artificial-intelligence-enabled workflows.
  • Experience supporting products deployed across multiple jurisdictions or operating environments.
  • Experience working on products that combine software, human services, staff workflows, and operational processes.
  • Familiarity with product experimentation, artificial-intelligence evaluation, or mixed-method product measurement.
  • Experience helping an organization move from one primary product toward a broader product portfolio.
  • Experience using artificial-intelligence-assisted tools to accelerate product discovery, prototyping, analysis, documentation, and testing.

About the Company

Think of Us

Data-driven foster care reform lab

Think of Us is a nonprofit research and design lab reforming the U.S. child welfare system. It partners with government agencies, lawmakers, and foundations to design and implement scalable, data-driven solutions at federal, state, and local levels, centering the lived experiences of foster youth and families. It uses technology, research, and design through programs like Virtual Support Services and a data-informed approach to prevent entry, promote kinship care, and support youth aging out. It is funded by grants and major philanthropic partnerships, with notable support including a $47.5 million, five-year commitment from The Audacious Project to raise $100 million total, and maintains a 40,000-person lived-experience database to co-design solutions.

Company size
11-50
Company stage
Grant
Total funding
$47.5M
Headquarters
Washington DC, District of Columbia
Founded
2014

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