Division: Detection
Start date: as soon as possible
Status: full-time SecureBio employee
Starting compensation: $185,000 to $210,000
Visa sponsorship available: For exceptional candidates
Work location: In-person in Cambridge, MA preferred; remote possible.
SecureBio is a nonprofit working to protect the world from catastrophic pandemics. Our Detection team is building a pathogen-agnostic early warning system using metagenomic sequencing of wastewater and human specimens. We’re seeking a quantitative researcher or epidemiologist to assess how reliably our surveillance system can detect emerging pathogens, and where to invest to improve it.
You’ll connect models of infection and disease spread with evidence from our sequencing workflows. Your models will answer key strategic questions: Are wastewater or nasal swabs a more effective sampling strategy for early pandemic detection? How does sensitivity increase as we scale our number of sites? Which sites should we pick? How deeply should we sequence each sample? You’ll estimate parameters from our data, build practical models in-house, and adapt and use collaborators’ complex epidemiological models.
We’re looking for someone rigorous and pragmatic, who enjoys analysis and making results useful to decision-makers. You’ll join our Computational Science team and work closely with laboratory scientists, leadership, and external modeling collaborators. Your work will inform our scientific priorities, scaling decisions, and funding proposals.
Build and update estimates of how early and reliably our system could detect emerging pathogens, across a variety of plausible outbreak scenarios
Use metagenomic data to estimate key parameters our models depend on (e.g., the effective number of people contributing to various sample types, and the rate of conversion from infections in the population to viral abundance in wastewater)
Build quick in-house models, and become an expert user of collaborators’ more complex epidemiological models.
Model tradeoffs across sampling strategies, sites, and sequencing depth
Share results early and often, building conviction across the team about what they mean
Work closely with external modeling collaborators, sharing our data and steering joint work toward practical answers
Turn around fast, honest analyses for leadership and funding proposals, and present them in terms funders and agencies recognize.
Use AI tools aggressively to move fast
A graduate degree in epidemiology, biostatistics, applied mathematics, statistics, or a related quantitative field (or equivalent experience)
5+ years of relevant work experience
Deep experience with quantitative epidemiology, infectious disease modeling, or similar real-world modeling problems.
Demonstrated ability to independently own substantive quantitative projects, from question formulation through communication of results
Proficiency in Python or R, and in good coding practices (version control, tests, documentation)
Strong quantitative judgment, including parameter estimation, uncertainty analysis, and “good taste” in selecting/building models
Pragmatic: start with simple models and share early results quickly
Intellectual honesty and willingness to challenge assumptions or conclusions, especially when results are inconvenient
Clear, concise communication and experience working with technical and nontechnical stakeholders
Ability to turn ambiguous questions into focused quantitative projects
Experience with wastewater epidemiology or similar environmental disease surveillance
Experience with metagenomics, environmental sequencing, or similarExperience evaluating system design or cost-effectiveness under uncertainty
Experience communicating quantitative findings to funders, public-health agencies, or government partners
Familiarity with methods such as compartmental (SIR/SEIR) and agent-based models
Experience analyzing stochastic epidemic or population-dynamics models mathematically (e.g., branching-process approximations, scaling arguments)
Has managed a collaboration with external researchers from start to finish, producing a concrete deliverable (such as a publication) that was useful to your team