Lead or co-author technical volumes for SBIR, OTA, and other non-FAR-based solicitations targeting medical R&D.
Translate innovative concepts into clear, compelling proposal content, including problem statements, methodologies, data strategies, and expected outcomes.
Collaborate with SMEs, PI-level researchers, and technical staff to design competitive research strategies.
Prepare proposal components including white papers, quad charts, biosketches, past performance, and work plans.
Track relevant R&D opportunities from MTEC, NIH, DTRA, DARPA, etc., and help shape pursuit strategies.
Support or lead exploratory data analysis, model development, and statistical validation for R&D efforts in public/military health.
Leverage tools such as Python, R, or SQL to analyze biomedical, clinical, or sensor-based datasets.
Help define data collection protocols, study design, and analytic frameworks in research proposals and active projects.
Integrate AI/ML, data visualization, and cloud-based analytics platforms (e.g., Snowflake, Databricks) as appropriate.
Collaborate with external researchers and partners to refine hypotheses and ensure methodological rigor.
Master’s or Ph.D. in Data Science, Public Health, Biostatistics, Computer Science, Biomedical Engineering, or related field.
3+ years of experience writing successful proposals for SBIR, OTA, or BAA funding in the federal health R&D space.
Hands-on experience analyzing complex health datasets (EHR, wearable data, surveys, genomics, etc.).
Proficiency in Python, R, or SQL for data manipulation, modeling, and visualization.
Ability to synthesize technical and scientific content for both technical and non-technical reviewers.
Strong communication, collaboration, and time management skills.
Prior experience supporting MTEC, SBIR initiatives.
Familiarity with DoD acquisition and research protocols, human subjects protection, or FDA regulatory pathways.
Understanding of AI/ML techniques applied to healthcare (predictive modeling, NLP, imaging).
Working knowledge of Snowflake, Databricks, AWS, or similar cloud-based data platforms.
Elsevier
SynergisticIT
Atticus
KoBold Metals
CrowdStrike