Senior Strategy Data Scientist

Provn; Dallas (TX); On-site; USD 105,000 - 140,000; Full time

Benefits offered by this job

Health, dental, and vision insurance

Retirement savings plan with employer

Generous paid time off and holidays

Professional development and learning

Job summary

CPAL (Child Poverty Action Lab) in Dallas, TX, is seeking a Senior Strategy Data Scientist to turn messy analyses into decisions that redirect resources for kids and families. This role sits on Strategic Analytics and reports to the Head of Strategic Analytics, with onsite preference and remote-first consideration.

You will frame questions, select methods, and deliver actionable insights for program teams and partners. Strong Python/SQL skills and a quantitative background are essential.

Qualifications

• Strong quantitative foundation with demonstrated analytical capability.
• 3–5 years of applied analytical work in strategy or data science roles.
• Proficiency in Python and SQL; ability to read and adapt existing R code.

Responsibilities

• Conduct end-to-end analyses, combining data, methods, and documentation.
• Frame the question, define the smallest useful version, and deliver actionable insights.
• Communicate findings clearly to non-technical decision-makers and stakeholders.

Skills

Python

SQL

R

Geospatial analysis

Data visualization

Causal inference

Machine learning

Education

STEM degree

Quantitative social science background

Economics

Urban or spatial analytics

Tools

R

AI-assisted tools

Job description

Senior Strategy Data Scientist CPAL (Child Poverty Action Lab) Dallas, TX · Full-time

Strategy questions here rarely arrive clean. No settled methodology, no well-formed problem statement, just a decision that needs to get made. This role turns messy analysis into decisions that redirect real resources for kids and families in Dallas.

At the Child Poverty Action Lab, 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. One mission drives all of it: cutting childhood poverty in Dallas by 50% within a single generation. Five big bets carry that mission forward: Benefits Delivery, Maternal Health, Housing, Criminal Justice, and Public Safety, each grounded in evidence connecting childhood experience to adult economic outcomes.

Five design principles guide the work. Start with children and families, then work backward to systems. A problem well stated is half a solution, so systemic issues get made concrete, actionable, and replicable before anyone tries to fix them. Systems are like a string of Christmas lights: one broken handoff, missing data, a confusing process, takes the rest down, and finding it is a repeatable exercise. Have a bias for action. Perfect is the enemy of good, so the team moves on the best available information rather than waiting for certainty. Test, learn, iterate. Experiment fast, build feedback loops, amplify what works.

This role sits on Strategic Analytics, reporting to the Head of Strategic Analytics. The team exists to find leverage: turning data into insight that helps CPAL and its partners direct limited resources where they'll improve the lives of the most children. It's small and senior, built to combine analytical rigor, practical judgment, and AI-enabled ways of working into a genuine strategic thinking partner for the program teams on the ground. Based in Dallas, TX. Onsite is preferred, but CPAL will consider remote-first candidates with occasional time on site.

Senior Strategy Data Scientists take on questions that rarely arrive with clean data, a settled methodology, or even a well-formed problem statement. You 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. Consulting, not academic. Get to a good answer fast, not a 100% answer too late. The areas span housing, economic mobility, maternal health, education, and public safety. Deep expertise in all of them isn't the bar. Learning fast is: picking up unfamiliar domains quickly, working confidently with imperfect data, and communicating sophisticated ideas clearly to non-technical decision-makers.

Day to day, you conduct analysis end to end: combining messy data, selecting appropriate methods, testing findings, documenting sources, definitions, assumptions, and limitations. You frame the question before you analyze it. That means finding the decision behind the request, then defining the smallest useful first version. And you go past the literal answer: explaining what the result means, surfacing adjacent insights, raising the questions stakeholders didn't know to ask. Each output gets built to be reusable, so the next related question is cheaper to answer. You work AI-natively too, using AI across research, coding, QA, and documentation. But you verify it carefully. Trusting it blindly isn't the job.

You fit if you bring a strong quantitative foundation. A STEM degree works, so does an applied quantitative social science background, economics, urban or spatial analytics, operations research, causal inference and program evaluation, population health analytics, or comparable demonstrated capability. Add 3 to 5 years of applied analytical work, typically at a top-tier strategy, boutique data science, or analytical consulting firm. An equivalent trajectory with unusual early responsibility, running analytics for a high-growth startup or agency, fits too.

You're professionally fluent in Python and working SQL, and you can read and adapt existing R. What separates a strong applicant here isn't the tool list. It's sound methodological judgment paired with consultant-style pragmatism: picking 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, is part of that same judgment. High agency and intellectual curiosity matter. So does a serious commitment to CPAL's mission, more than prior nonprofit experience does, which isn't required. Geospatial analysis and data visualization experience is a strong plus.

CPAL is open to sponsoring the right candidate.

Compensation for this role is competitive, dependent on experience.

• health, dental, and vision insurance
• a retirement savings plan with employer match
• generous paid time off and holidays
• professional development and learning support

You'll 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 comes with it, so your analysis reaches decision-makers, not a dashboard backlog. AI-native ways of working: actively supported, actively expected.

CPAL is an equal opportunity employer and considers 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, CPAL will work with you to provide it.