Senior Strategy Data Scientist

About CPAL

At the Child Poverty Action Lab (CPAL), we believe every child deserves a life filled with opportunity. CPAL operates as an unofficial research and development lab for Dallas — using data to rethink public systems and equipping neighborhood-level partners to succeed, in service of one mission: cutting childhood poverty in Dallas by 50% within a single generation.

We work across five "big bets": Benefits Delivery, Maternal Health, Housing, Criminal Justice, and Public Safety. Each is grounded in evidence connecting childhood experience to adult economic outcomes.

Five design principles guide the work:

• Start with children and families, and work backward to systems. Understand what families actually experience, then reverse-engineer the policies and processes around them.
• A problem well stated is half a solution. Systemic problems stay intractable until they are made concrete, actionable, and replicable.
• Systems are like a string of Christmas lights. One broken handoff — missing data, a confusing process — takes the rest down. Finding and fixing it is a repeatable exercise.
• Have a bias for action. Perfect is the enemy of good. We move with the best available information rather than waiting for certainty.
• Test, learn, and iterate. We experiment fast, build feedback loops, and amplify what works.

About the Role

Department: Strategic Analytics · Reports to: Head of Strategic Analytics

Our Strategic Analytics team exists to find leverage: turning data into insight that helps CPAL and its partners direct limited resources where they will improve the lives of the most children. We are building a small, senior team that combines analytical rigor, practical judgment, and AI-enabled ways of working — a strategic thinking partner for the program teams on the ground.

Senior Strategy Data Scientists take on questions that rarely arrive with clean data, a settled methodology, or even a well-formed problem statement. You will work out what decision needs to be made, what evidence would actually be useful, what can credibly be answered, and how to deliver value without overengineering the solution. The mindset is consulting rather than academic: get to a good answer fast, not a 100% answer too late.

You will work across areas including housing, economic mobility, maternal health, education, and public safety. Deep expertise in every field is not expected. Success requires learning unfamiliar domains quickly, working confidently with imperfect data, and communicating sophisticated ideas clearly.

What You'll Do

• Conduct analysis end to end. Combine messy data, select appropriate methods, test findings, and document sources, definitions, assumptions, and limitations.
• Frame the question before analyzing it. Find the decision behind the request, challenge flawed assumptions, and define the smallest useful first version.
• Produce more than the literal answer. Explain what the result means, identify adjacent insights, and surface the questions stakeholders did not know to ask.
• Connect analysis to decisions. Produce work that can move resource allocation, program design, or organizational strategy.
• Communicate clearly. Turn complex analysis into concise briefs, visuals, and explanations that non-technical decision-makers can use with external stakeholders.
• Build institutional knowledge. Make each output reusable so the next related question is easier and cheaper to answer.
• Work AI-natively. Use AI across research, coding, QA, and documentation — and verify it carefully rather than trusting it blindly.

What Success Looks Like in Your First Year

• Your analysis has influenced resource allocation, program design, or external strategy across multiple issue areas.
• Your work is reproducible, can be understood or extended by another data scientist, and has been contributed to the institutional library.
• Your AI-assisted workflows have materially improved speed or quality without sacrificing rigor.

Location

Dallas preferred; flexible for the right candidate. Remote-first candidates are considered, with occasional on-site time in Dallas.

Hiring Process

This role includes a short practical challenge — roughly 45 minutes (30 minutes of analysis plus a written email and a recorded video walkthrough). It uses a real, unreviewed data file and asks you to answer the kind of question a program leader would actually send you. You must complete the challenge to be considered.

• A strong quantitative foundation — a STEM degree, an applied quantitative social science background (economics, urban science or spatial analytics, operations research, causal inference and program evaluation, population health analytics), or comparable demonstrated capability.
• 3–5 years of applied analytical work, typically at a top-tier strategy, boutique data science, or analytical consulting firm. Equivalent trajectories with unusual early responsibility — running analytics for a high-growth startup or agency — also fit.
• Professional fluency in Python and working SQL; able to read and adapt existing R.
• Sound methodological judgment paired with consultant-style pragmatism: you pick the approach that fits the question, the evidence, the timeline, and the stakes.
• Daily working fluency with AI-assisted analytical tools, backed by concrete examples.
• High agency, intellectual curiosity, and the ability to learn unfamiliar subject areas quickly.
• A serious commitment to CPAL's mission. Prior nonprofit experience is not required.

Strong plus: geospatial analysis and data visualization experience.

• Competitive salary commensurate with experience
• Health, dental, and vision insurance
• Retirement savings plan with employer match
• Generous paid time off and holidays
• Professional development and learning support
• Work with a small, senior team on problems that change outcomes for children and families in Dallas
• Direct access to program leaders and public-agency partners — your analysis reaches decision-makers, not a dashboard backlog
• AI-native ways of working actively supported and expected

CPAL is an equal opportunity employer. We consider all qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, genetic information, or any other characteristic protected by applicable law. If you need a reasonable accommodation at any point in the application or interview process, let us know and we will work with you to provide it.