Gryphon Scientific is a 50-person research organization with a unique focus on physical and life sciences, public health, and emergency management. Our aim is to improve the health and safety of populations world-wide. Our projects drive policy changes in security, preparedness, and science policy at the highest levels of the US government. Abroad, we engage with international stakeholders to strengthen the capabilities of developing countries to address public health and security challenges. The energy of our small business environment, as well as our commitment to technical excellence and advanced analytic approaches, attract staff with the highest qualifications. Gryphon strongly encourages collaboration throughout the company, between junior and senior staff and across disciplines. Personal and professional development are actively supported. Gryphon boasts a laid-back workplace, a comprehensive employee benefit package, and places an emphasis on work-life balance, which has resulted in excellent employee retention. We are growing and looking for new staff members to join the Gryphon team.
As a Data Scientist, you will be responsible for serving as a team member and leader on analytical and modeling tasks, developing solutions related to public health and safety, homeland security, international engagement, and emerging infectious disease, under the guidance of project principal investigators. You will lead data science analysis tasks and supervise junior data scientists, developers, and statisticians. Projects incorporate a variety of approaches—from data analytics and computational modeling to policy evaluation and stakeholder engagement—to guide decision making by leaders in emergency preparedness and response, healthcare and biomedical science, and biosecurity and defense. Gryphon Scientific offers data scientists the opportunity to use cutting edge technologies, critical thinking, creative problem solving, and analysis to tackle challenging projects in a wide range of exciting fields. Each project will challenge you to think, research, analyze, and write in new ways.
· In collaboration with the data science team, become a data science expert in topics including but not limited to infectious diseases, global health security, biosecurity, emergency preparedness and response, biotechnology, and/or public health.
· Provide thought leadership on appropriate data science and quantitative modeling methods across a wide array of existing projects and proposals for future projects.
· Seek out and define new data collection and analysis strategies.
· Design, implement, test, and validate statistical, artificial intelligence, and machine learning techniques and models.
· Collaborate with team members to develop technical approaches, interpret research results, and formulate next steps.
· Document and present complex quantitative analysis and results to internal and external stakeholders at the appropriate technical depth via written reports and oral presentations.
· Undergraduate degree in a relevant discipline (Biological Sciences, Statistics, Computer Science, Data Science, Mathematics or a related field) and 5 years of data analytics or data science experience
· GPA greater than 3.0 (out of 4.0) in your major.
· Experience conducting research and analysis on large data sets at the undergraduate, graduate, or professional level.
· Preference given to candidates in or willing to re-locate to the greater Washington, DC, area. Remote work considered for candidates located in the United States (must maintain east coast hours).
· U.S. citizenship.
Required Skills: (include technical, managerial and other skills)
· Experience building data science analysis solutions on large scientific data sets in R or Python.
· Experience searching and accessing data via popular database technologies, such as PostgreSQL.
· Demonstrated experience in designing and implementing rigorous methods for statistical analysis of observational and experimental data.
· Demonstrated experience in data cleansing, normalization, and standardization.
· Demonstrated experience in supervised and unsupervised machine learning techniques including regression and classification.
· Proficiency in methods of scientific research, including designing and executing a literature search to address a research question, collecting, and organizing data from published sources, and interpretation of data.
· Strong critical thinking and problem-solving skills.
· Strong qualitative and quantitative analytical skills.
· Strong communication and interpersonal skills.
· Strong attention to detail; organization skills and excellent follow-through.
· Excellent writing and oral communication skills.
Additional Skills and Experience (desired but not required):
· PhD in a quantitative research setting within the biological or physical sciences.