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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Data Scientist - **Company:** JRSS, INC. - **Location:** Atlanta, GA, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Automation of Tests, Microsoft Azure, Cloud Computing, Code Review, Computational Biology, Continuous Integration, Open Source Technology, Git, Software Coding, Software Version Control, Docker - **Published:** September 19, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pl4t8nm9t9 ## About the Role * Master's or doctoral degree in Statistics, Biostatistics, Applied Mathematics, Epidemiology, Computational Biology, Operations Research, or a closely related quantitative discipline. * 8+ years of applied statistical modeling experience, or 5+ with a doctoral degree, including work that went into production or operational use. * Expert-level R, including writing code others will run and maintain; working production proficiency in Python. * Demonstrated Bayesian inference experience - Stan, NumPyro, PyMC, or equivalent - including hierarchical models and MCMC diagnostics. * Direct experience with infectious disease or epidemiological modeling, or equivalent mechanistic modeling of a dynamic process. * Time-series forecasting with rigorous evaluation: proper scoring rules, calibration, backtesting. * Experience with messy operational reporting data - delays, right-truncation, revisions. * Software engineering fundamentals: Git workflow, code review, automated testing, Docker, CI/CD. * Cloud compute experience; Azure preferred. * Ability to obtain and maintain a U.S. federal Public Trust or Suitability/Fitness determination. Nice to have * PhD with peer-reviewed publications in infectious disease modeling, forecasting, or statistical methodology. * Hands-on experience with the open-source epidemiological modeling ecosystem - EpiNow2, epinowcast, EpiEstim, scoringutils, or comparable tooling. * Prior participation in a collaborative forecasting hub as a contributing modeling team. * Prior federal public health, national laboratory, or federally funded research experience. * Direct experience supporting an active outbreak response under operational time pressure. * Public open-source contributions, particularly to statistical or epidemiological software. * Experience mentoring analysts or leading a small technical team. ## Description * Design, implement, and validate Bayesian models for epidemic situational awareness - renewal-equation and compartmental (SEIR-family) models, Rt estimation, nowcasting under reporting delay, and short-term forecasting. * Own production modeling pipelines end to end: R and Stan model code, containerized execution, cloud batch orchestration, structured outputs, and run diagnostics. * Contribute models and evaluation to collaborative forecast hubs, including ensemble construction and scoring across contributing teams. * Build scenario models and outbreak simulators that quantify the effect of candidate interventions. * Set the standard for forecast evaluation - proper scoring rules, calibration and coverage, and backtesting against retrospective data. * Work with surveillance data at source, including its delays, revisions, and biases. * Communicate uncertainty clearly and honestly to epidemiologists, policy staff, and senior leadership - including where the data don't support a conclusion. * Set engineering standards for the team: version control, code review, automated testing, reproducible environments, and open-source release practice. * Mentor junior and mid-level data scientists, and collaborate with external academic and public health partners. ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Are Code Reviews Worth It? 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