Secure a Two-Year CDC PE Fellowship (2026): GS-12 Paid Postdoc in Public Health Economics, Modeling, and Data Science

If you want a postdoctoral position that puts you at the intersection of policy, dollars, and population health, the CDC Steven M. Teutsch Prevention Effectiveness (PE) Fellowship is one of the few programs that actually delivers that mix. This is a two-year, intensely practical fellowship placed mostly at CDC headquarters in Atlanta, where fellows work on projects that quantify the health and economic impacts of public health decisions. In plain English: you’ll use math, data, and economic reasoning to answer questions like “Which vaccine strategy saves the most lives per dollar?” or “Is screening worth the cost?” and your answers can influence national policy.

The program accepts roughly 20 fellows a year — about 10 in the Traditional Track and 10 in the Analytics and Modeling Track — so it’s selective but not mythical. Fellows are federal employees for the fellowship term, paid at the General Schedule (GS) 12, step 3 level, and receive CDC employee benefits. You won’t bring your own grant money or fundraise; you’ll be embedded in teams that have real data and real policy timelines. If you like the idea of turning models and statistics into policy memos that people read and act on, this is one of the best stepping stones.

This guide breaks down what the PE Fellowship actually offers, who should apply, how reviewers evaluate applications, and step-by-step advice to make your application competitive. I’ll also include a realistic timeline and a checklist of required materials so you can take action now and not in a panic the week of the deadline.

At a Glance

What This Opportunity Offers

This fellowship is practical training in decision science and economic evaluation applied to public health. The two-year structure gives you time to contribute meaningfully to projects and to build a visible portfolio of analytic work aimed at real-world problems.

Financially, you’re a federal employee at a mid-career pay grade — a meaningful detail when you compare postdoc stipends that can vary widely. Beyond salary and healthcare, the position provides access to CDC data, collaborators across epidemiology, economics, and modeling, and mentorship from senior scientists who work at the policy interface. That exposure is the core value: you’ll learn how analyses are used (and sometimes misused) by decision-makers, how to write concise policy briefs, and how to present quantitative evidence clearly to nontechnical audiences.

The fellowship also gives practical experience in a range of methods: cost-effectiveness analysis, budget impact modeling, dynamic infectious disease modeling, decision trees, Markov models, economic evaluation with observational data, and advanced statistical methods. Depending on your placement, you might help design models that inform vaccine recommendations, estimate healthcare costs attributable to a disease, or project outcomes for prevention strategies. The work often moves faster than academia — you’ll need to produce succinct, defensible results under time pressure.

Finally, the program is a powerful credential. Graduates go into academic positions, state and federal public health agencies, NGOs, and industry roles where policy-relevant analytic skills are valued. If your goal is to influence public health policy with quantitative evidence, the PE Fellowship gives you both the technical training and the network to do that.

Who Should Apply

This fellowship isn’t for every PhD. It’s strongest for candidates who combine rigorous quantitative training with a clear interest in public health policy. For the Traditional Track, successful applicants typically hold a PhD in economics, agricultural economics, health services research, public health with a quantitative emphasis, or a closely related field. You should be comfortable with microeconomic concepts, causal inference, and cost-effectiveness frameworks.

For the Analytics and Modeling Track, the sweet spot includes those trained in epidemiology, infectious disease modeling, applied mathematics, operations research, industrial engineering, biological sciences with quantitative emphasis, and data science. If you’ve built compartmental models (SEIR-style), agent-based simulations, or optimization models for resource allocation, that experience maps well to this track.

Practical examples of a good fit:

• A recent PhD in health economics who has run cost-effectiveness models for treatment strategies and has a couple of peer-reviewed papers or strong working papers.
• A computational epidemiologist who developed and validated transmission models during PhD work and can explain calibration and uncertainty analysis.
• A data scientist with experience in causal inference using administrative healthcare data who can estimate healthcare utilization and costs.

You don’t need an appointment offer or existing CDC connection to apply — but you must have all degree requirements completed before the fellowship starts. F1 visa holders can apply, but must have OPT/EAD valid through the first year plus STEM eligibility; expect additional work authorization conversations if you progress in the selection process.

Insider Tips for a Winning Application

This is where many applicants stumble: they show technical chops but fail to demonstrate how those chops translate into policy-relevant outputs. To stand out, craft an application that blends technical credibility with clarity about impact and teamwork.

1. Tell stories with numbers. The reviewers are quantitative, but they’re also busy. Use your personal statement to narrate one or two projects where your analyses changed a decision, clarified trade-offs, or revealed surprising results. Be specific: what question did you answer, what methods did you use, what was the conclusion, and how did that conclusion matter?

2. Emphasize reproducibility and code. Host a polished repository (GitHub or similar) with scripts, documentation, and a short README for at least one representative analysis. If you can show that your code produces the figures or results you describe, reviewers take that as evidence of rigor and practical skill.

3. Choose referees who will say more than “great researcher.” Ask letter writers to speak to your ability to work on multidisciplinary teams, communicate with nontechnical audiences, and deliver under deadlines. A glowing technical endorsement is necessary but so is a concrete line about your collaboration and communication style.

4. Match methods to track. If you’re applying to the Analytics and Modeling Track, highlight model calibration, sensitivity analysis, handling of stochasticity, and experience with modeling software (e.g., R, Python, C++, AnyLogic, Berkeley Madonna). If you’re applying to the Traditional Track, show econometric rigor, cost-effectiveness frameworks, and familiarity with health policy contexts.

5. Provide a short policy brief sample. Create a 1-2 page policy brief based on one of your analyses that explains the question, data, top-line findings, and policy implications. Make it crisp. If reviewers can see that you can translate technical work into actionable recommendations, you’re already ahead.

6. Demonstrate breadth, not just depth. The CDC wants fellows who can operate across disciplines. If you have experience collaborating with clinicians, health economists, and modelers — describe the collaboration and your specific contribution.

7. Clean, concise writing wins. The personal statement is often the first thing reviewers read. Edit ruthlessly. Use active voice, avoid jargon, and aim for clarity. If you can explain a complex method in a paragraph that a smart non-expert will understand, you’ll impress reviewers.

8. Prepare for interviews by practicing timed presentations. The selection process can include interviews and technical discussions. Be ready to present a 10–15 minute overview of a past project and defend methodological choices under questioning.

Taken together, these steps move your application from “technically competent” to “ready to contribute on day one.”

Application Timeline (Realistic, Work-Backwards)

Start at least 10–12 weeks before January 9, 2026. Quality applications take time.

• Week 0 (January 9, 2026): Application deadline — submit at least 48 hours early to avoid last-minute portal problems.
• Week -1 to -2: Final proofreading, gather submission signatures (if institutional approvals are needed), and upload materials.
• Week -3 to -4: Circulate final drafts to letter writers and ask them to submit letters by your internal deadline. Finalize GitHub repo and policy brief.
• Week -5 to -8: Draft personal statement and project summaries. Get feedback from advisors and senior colleagues outside your immediate subfield.
• Week -9 to -12: Identify and confirm recommenders, outline your personal statement and project descriptions, begin assembling documents and transcripts. If you’re an F1 student, ensure OPT/EAD timing is in order and get paperwork started.
• Months before (recommended): Contact current or former PE fellows for informational interviews. They can give realistic advice about placements and expectations.

Selection and notification timelines vary, but expect reviews and interviews to take place several weeks to a few months after the deadline. Plan for potential relocation in the months after notification.

Required Materials (and How to Prepare Them)

The official application portal lists required materials, but here’s what successful applicants typically include and how to make each item count.

• Curriculum vitae (CV): Keep it concise and focused on relevant skills — modeling, software proficiency, leadership in collaborative projects, publications, and teaching. Use bullet points for project descriptions and include links to code repositories and working papers.
• Personal statement (or cover letter): This is your narrative. Explain why the PE Fellowship is the logical next step in your career, what you hope to accomplish, and which methodological skills you bring. Include one or two specific project examples and (if possible) the type of CDC placement you prefer.
• Letters of recommendation: Ask for specific, duty-focused letters that spell out achievements, technical competence, and your ability to work in policy settings. Provide letter writers with a short brief describing the fellowship and points you’d like them to emphasize.
• Writing sample(s): Include a short policy brief or a condensed version of a paper that demonstrates your ability to translate analysis into recommendations. If your primary evidence is code, include a short write-up that explains the model and findings.
• Code or portfolio links: A public GitHub repository with a clean, documented example of your work speaks louder than a long list of technical skills. Include data (or simulated data if real data are restricted) so reviewers can run your code.
• Transcripts and degree verification: Ensure your PhD requirements will be completed prior to the start date. Have official or unofficial transcripts ready if the portal requests them.
• Visa/immigration documentation (if applicable): F1 applicants must have OPT/EAD valid for the entire first year and STEM eligibility. Prepare copies of your EAD and OPT documentation and be ready to discuss timing if you advance.

Prepare every document with an eye toward clarity. Replace dense academic prose with crisp, policy-oriented language where appropriate.

What Makes an Application Stand Out

Reviewers evaluate a combination of technical ability, evidence of productivity, and promise as an applied researcher. Several attributes consistently set funded applicants apart.

Academic preparedness: Demonstrated by a strong PhD program, relevant coursework, and an understanding of both theory and empirical methods. A candidate who can show depth in econometrics, cost-effectiveness analysis, or model calibration scores highly here.

Letters of recommendation: Specificity matters. Letters that quantify contribution (“led development of a compartmental model used in 3 peer-reviewed analyses”) and comment on soft skills (communication, mentorship, responsiveness) carry weight.

Research and work experience: Prior internships, collaborations with health agencies, or relevant postdoc experience are big pluses. If you have experience working with health claims data, national surveillance datasets, or EHRs, make that clear.

Publications or publication potential: Having peer-reviewed work helps, but solid working papers, preprints, or conference presentations that show you can produce and communicate science also count.

Methodological and data skills: Show mastery of the tools of the trade — statistical packages, modeling languages, simulation frameworks — and demonstrate familiarity with best practices like uncertainty quantification and sensitivity analysis.

Personal statement and fit: The statement should explain why you want the PE Fellowship specifically and how the CDC setting will let you grow. Applicants who explain use cases, potential project ideas, and how they would contribute to CDC teams often do better.

Team and communication skills: Because CDC work is collaborative, evidence that you can explain methods non-technically and work with multidisciplinary teams is crucial.

Common Mistakes to Avoid (and How to Fix Them)

Mistake 1 — Technical blizzard with no meaning: Some applicants pack the statement with equations and jargon but fail to explain the policy question. Fix: Start with the policy problem, then describe the method you used and why it was the right tool.

Mistake 2 — Generic letters: Letters that repeat the same bland praise aren’t helpful. Fix: Give letter writers a one-page list of specific contributions you want them to highlight (projects, leadership, communication examples).

Mistake 3 — No code evidence: Claiming modeling skills without providing any reproducible example raises skepticism. Fix: Publish one clean example on GitHub with a small dataset and instructions.

Mistake 4 — Weak tailoring: Submitting the same application to both tracks without adjusting emphasis is common. Fix: Tailor your personal statement and samples to the chosen track — economics and policy for Traditional, model calibration and simulation for Analytics & Modeling.

Mistake 5 — Ignoring visa timing: F1 applicants sometimes apply without confirming OPT/EAD coverage for year one. Fix: Confirm OPT dates and STEM extension eligibility well before applying and include proof if requested.

Mistake 6 — Sloppy presentation: Grammatical errors, inconsistent formatting, and broken links create a poor impression. Fix: Proofread carefully and have at least two colleagues review the full package.

Frequently Asked Questions

Q: How many fellows does the program select each year? A: Historically the program selects about 20 fellows: roughly 10 for the Traditional Track and 10 for the Analytics and Modeling Track. Exact numbers can vary by cycle.

Q: What does GS-12, step 3 pay mean in practice? A: The fellowship pays at the federal General Schedule GS-12, step 3 level. Federal pay tables and locality adjustments determine final salary (Atlanta locality may apply). Check current federal pay tables or the CDC HR office for the exact annual salary for the year you’ll start.

Q: Can international students on F1 visas apply? A: Yes, F1 students can be considered if they have OPT with an EAD valid for the entire first year and STEM OPT eligibility. Non-US citizen applicants who advance will be contacted regarding work authorization specifics.

Q: Will I have to relocate to Atlanta? A: Most fellows are placed at CDC headquarters in Atlanta. Some placements may be with state health departments or partner agencies. Fellows are responsible for relocation expenses.

Q: Is prior public health experience required? A: Not strictly, but demonstrated interest and some relevant experience (internship, collaboration, or coursework) strengthen an application. The CDC values quantitative skill and the ability to apply it to public health problems.

Q: What kinds of projects do fellows work on? A: Examples include vaccine strategy evaluations, cost-effectiveness of screening programs, modeling transmission dynamics, economic burden estimates, and budget impact analyses. Expect a mix of modeling, data analysis, and policy-focused writing.

Q: Do fellows get mentorship? A: Yes. Fellows are embedded in teams and receive mentorship from CDC scientists and program managers, which is a key part of the program’s training value.

Q: Can I apply to both tracks? A: Check the current application instructions on the official page. If allowed, tailor each submission to emphasize the qualifications and examples relevant to the chosen track.

Next Steps / How to Apply

Ready to begin? Do these five concrete things in this order:

1. Read the official announcement on the CDC portal to confirm details and the deadline: https://cdc-efms.powerappsportals.us/programs/details/start/?appFormId=38f060ff-a673-f011-bec2-001dd804034b
2. Decide which track fits your background (Traditional or Analytics & Modeling) and outline 2–3 projects you’d highlight in your personal statement.
3. Contact prospective letter writers now. Give them your CV, a one-page summary of the fellowship, and bullet points you’d like them to cover.
4. Prepare a short, polished policy brief and a GitHub repo with a reproducible example of your work.
5. Register in the application portal, assemble transcripts and visa documentation (if applicable), and plan to submit at least 48 hours before January 9, 2026.

Apply Now / Full Details Visit the official opportunity page and submit your application here: https://cdc-efms.powerappsportals.us/programs/details/start/?appFormId=38f060ff-a673-f011-bec2-001dd804034b

If you want, tell me in one paragraph about your background (field, key skills, one project) and I’ll suggest how to angle your personal statement for the right track.