Senior Strategy Analyst
for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we should map content to those headings if present. There's "About the role" heading present. There's "What you'll do" which is responsibilities. There's "What success looks like in your first year" maybe also responsibilities or qualifications? There's "Location" maybe schedule? There's "Hiring process" maybe not needed. There's "What we're looking for" which is qualifications/requirements/skills. There's "Bonus if you have" maybe skills. There's "Benefits and perks" which includes salary and benefits. There's "Pay" maybe salary. There's "Schedule" maybe hybrid? There's "Location" maybe schedule? Actually schedule refers to work schedule (full-time, hybrid). The posting says "Full time | Hybrid". So schedule: Full-time, hybrid. Pay: Salary range $100,000 to $140,000, commensurate with experience. Benefits: list.
We must not include sections with filler like "commensurate with experience" as heading content? Actually we can include Pay section only if posting states them. It does state salary range. So we include Pay section with that info. Benefits section: list benefits.
Schedule: we have "Full time | Hybrid". So schedule section.
Now we need to avoid repeating job title. Not include "About CPAL" as heading? Could we include as a paragraph under About the role? The instruction: Use for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It doesn't forbid other headings, but we should likely stick to those. However we have "About CPAL" content that describes organization. Could we put that under About the role? Probably not; About the role is about the role. But we could include a brief intro paragraph before headings. The instruction: Output ONLY the HTML fragment — no /, no markdown code fences. So we can have a leading
describing CPAL? It's not a heading. It's permissible as prose. The instruction says use
for prose. So we can include a paragraph about CPAL before the headings. We must not include boilerplate like EEO, accommodation, etc. Remove those. We must preserve all substantive information. So we need to include details about role, responsibilities, qualifications, etc. Let's extract: About CPAL paragraph: "Every child deserves a life filled with opportunity. CPAL operates as an unofficial research and development department for Dallas, rethinking how data can be integrated into public systems, community programs, and neighborhood life to break cycles of intergenerational poverty. The mission is specific and measurable: cut child poverty in Dallas by half within a single generation. Child poverty is a problem of a scale that only public budgets can fund solutions to match, so CPAL convenes the leaders of nine Dallas public agencies with a combined annual operating budget of more than $10 billion, alongside more than 100 partner organizations, and points those resources at the interventions evidence supports. The work spans five big bets: Benefits Delivery, Maternal Health, Housing, Criminal Justice, and Public Safety." About the role: paragraph: "The Senior Strategy Analyst is a consultative role rather than an academic or reporting one. We are not looking for a data scientist to build machine learning pipelines or architect databases. We are looking for someone who can get to a defensible answer quickly, under real-world time pressure, and who knows when the answer approximates truth well enough to act on. You will work with independence and judgment, while collaborating closely with internal teams and community partners." Requests reach you... etc. That's part of responsibilities. What you'll do: list of responsibilities bullet points. Let's capture responsibilities: - Turn a fuzzy ask into an answerable question. Find the decision behind the request, isolate what is measurable, and define the smallest useful first iteration instead of getting bogged down in endless analysis. CPAL starts with children and families and works backward to systems, which is what keeps an analysis attached to what a family experiences on the ground. - Conduct applied data analysis using publicly available and administrative datasets. ACS, CDC WONDER, Department of Education, criminal justice data, TX HHSC, Dallas ISD, Feeding America, internal program data, and others, with a working understanding of their quirks: complex sampling, vintage issues, sample weights, missingness, and outliers. Interrogate an unreviewed dataset before you build on it, because most of the risk in this job lives in data nobody has checked yet. - Trace a system to the point where it breaks. CPAL views public systems like a string of Christmas lights, where one dropped notification or missing form takes the rest of the line down. Follow a multi-agency workflow to where families fall out of it, then say what that means for budgets, policy levers, and operations, including where dollars and frontline staff should go next. - Go beyond the literal ask. Tell the requester what else they need to know, flag the confounders, and say plainly what they should not claim publicly because the data will not support it. - Translate findings into clear, responsible communications for stakeholders who are not statisticians. Help them understand what the data supports and what it does not. Communicate like a consultant, not an academic: a one-page memo or brief, with a visual where it helps, that a program lead can use in front of a reporter, a funder, or a city partner today, and that reads for a city council member, police leadership, a hospital director, or a community organizer. Clarity over aesthetic flair. - Work AI-natively, and verify like it is your name on the answer. Use AI across research, coding, and QA. The framing, the judgment, and the final synthesis stay yours. AI drafts; you decide. - Produce clean, well-documented, reproducible workflows. Record your assumptions, sources, and what you checked, so your work can be understood, audited, and built on by others, and so the next related question is cheaper to answer. What success looks like in your first year: - Your analysis has changed a resource-allocation decision, a program design choice, or an external message in more than one issue area. That might be a housing affordability analysis becoming the fact base a city partner argues from, or an enrollment gap that sends outreach to a few neighborhoods instead of the whole city. - Program leads bring you the ambiguous questions, not just the clean ones. - Your AI-assisted workflows have made you faster without making your answers less trustworthy. You can always say what you checked and why. Location: "This role is based in Dallas and works hybrid, with regular time in the office alongside the team and CPAL's public-agency partners." Hiring process: details about practical challenge etc. Might be considered part of application instructions, which we should remove per instruction: "Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions." So we should remove hiring process details (the practical challenge). So we omit that. What we're looking for: list of qualifications/requirements/skills. Let's capture: - More than anything, we are looking for a particular kind of analytical instinct. Can you defend your choice of denominator when there were three reasonable options? When a tract-level estimate carried a margin of error wide enough to swallow the difference someone was asking about, did you aggregate up, caveat hard, or tell them the comparison could not be made, and can you walk another person through why? We want someone who has made calls like that and can explain them. - 3 to 4 or more years of applied experience using data to answer real-world questions. Not just running models, but knowing which model to run and why. - Fluency in at least one analytical programming language: R, Python, Stata, or similar. Reproducible, well-commented code is a must. - Hands-on experience with complex, publicly accessible and administrative datasets, and a genuine understanding of what makes each one tricky. - Strong grounding in statistical methods: regression, sampling and weighting, hypothesis testing, missing data, and the limits of each. - The ability to work fast under pressure and to know when an answer is good enough to act on, without cutting the corners that matter. - Strong written and verbal communication, including the ability to explain methodological decisions to non-technical audiences. - A commitment to using data for good, carefully, transparently, and in service of communities. Bonus if you have: - Experience with program evaluation, quasi-experimental design, or causal inference methods. - Familiarity with geographic or spatial analysis. Poverty in Dallas is highly localized, and neighborhood-level work runs through it. - Experience in a nonprofit, government, or policy-adjacent environment, including the lags and quirks that come with public records. - Comfort with AI-assisted research workflows: prompt engineering, LLM-assisted coding or thematic analysis, and output validation. Benefits and perks: - Salary of $100,000 to $140,000, 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, so your analysis reaches decision-makers rather than a dashboard backlog - AI-native ways of working actively supported and expected Now we need to map to sections. We have: - About the role: we have a paragraph about the role (the consultative role). Could also include the "About CPAL" as a separate intro paragraph before headings. - Responsibilities: "What you'll do" and "What success looks like in your first year" maybe combined under Responsibilities. - Requirements/Qualifications/Skills: The "What we're looking for" and "Bonus if you have" could be split into Requirements (mandatory) and Skills (bonus) or Qualifications. We need to decide. We have headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to include only those with real content. We have content for About the role (the consultative role paragraph). We have Responsibilities (what you'll do + success). We have Requirements (3-4+ years experience, fluency in language, hands-on experience, strong grounding, ability to work fast, strong communication, commitment). We have Qualifications maybe overlapping; but we could treat Requirements as mandatory qualifications, and Skills as bonus items. Qualifications could be similar to Requirements; but we need to avoid duplication. Perhaps we put Requirements as the mandatory list, Skills as the bonus list, and Qualifications maybe we could omit if redundant. However we need to include only sections with real content; we can omit Qualifications if we feel it's duplicate. But we need to decide. The instruction: "Prefer a few substantive sections over many thin ones." So we could combine Requirements and Qualifications into one section maybe called Requirements (or Qualifications). But we have to use the headings exactly as given? It says use