Data Scientist

Candidates must be able to work onsite in the Washington, DC metropolitan area and have the ability to provide advanced analytical and technical data science expertise in support of our biomedical informatics research, clinical data analytics, and artificial intelligence initiatives.

This role will lead the development and application of statistical models, machine learning algorithms, and data analytics techniques to extract insights from large-scale biomedical and clinical datasets. Candidates will also support the development of secure clinical data systems and contribute to applied research projects advancing data-driven discovery and health informatics.

Position responsibilities include, but are not limited to:

• Advanced Data Analytics and Modeling
• Developing and implementing statistical and machine learning models for analysis of large biomedical and clinical datasets.
• Conducting predictive modeling, regression analysis, and other statistical techniques such as Cox regression and longitudinal modeling
• Applying artificial intelligence methods such as machine learning, deep learning, and natural language processing to biomedical data and research techniques.
• Utilizing emerging AI technologies, including large language models, to synthesize research findings and support knowledge discovery.
• Supporting the development and maintenance of systems for storing, managing, and analyzing health-related data, including the Federal Information Security Modernization Act moderate Environment for Health-Related Data about Individuals.
• Conducting data integration, harmonization, standardization, and data quality assessment across diverse datasets.
• Performing data linkage and longitudinal analysis of clinical and healthcare data including electronic health records and claims data.
• Developing dashboards, reports, and visualizations to communicate analytical findings to scientific and program stakeholders.
• Utilizing tools such as Tableau, Python, R, Databricks, Spark SQL, and related platforms.
• Documenting analytical methods, research findings, and technical outputs for internal and external reporting.
• Collaborating with software engineers and our DevOps teams to integrate analytical models into scalable platforms and applications.
• Providing technical guidance and mentorship to junior data scientists and analysts.
• Leading data science tasks within multidisciplinary project teams.
• Contributing to technical research publications and presentations as appropriate.

Prospective Candidates must:

• Hold a Master’s degree in data science, biostatistics, computer science, bioinformatics, applied mathematics, or related quantitative field (Ph.D. preferred).
• Have a minimum of 5 years of experience performing advanced statistical analysis, machine learning, or large-scale data analytics.
• Have demonstrated experience working with biomedical, clinical, or public health datasets.
• Be proficient in statistical and programming tools such as R, Python, SAS, and SQL as well as Databricks.
• Have Experience leading technical data science tasks or otherwise mentoring junior staff.
• Be able to collaborate with software engineers and DevOps teams

Preferably, candidates will also have experience working with HHS, NIH, or federal biomedical research datasets, be familiar with AI/ML frameworks, NLP techniques, deep learning methods, and big data analytics platforms (Spark, Databricks), and be knowledgable of clinical data standards and interoperability frameworks. Experience contributing to biomedical research publications, bioinformatics, or applied informatics research projects is a plus.

All applicants must be US Citizen and be able to obtain a US Government security clearance (Public Trust 5) within a reasonable period before employment begins.