Leidos Public Health Portfolio has an immediate opening for a Sr Data Scientist, contingent upon contract award, in support of a research project at the Centers for Disease Control and Prevention (CDC).

The Sr Data Scientist is responsible for leading a public health research project using an artificial intelligence (AI)–driven approach to community contact tracing and exposure notification specifically designed to enhance disease control within medical facilities. This project leverages existing electronic health record (EHR) data and advanced cloud computing resources to automate and enhance the tracking of patient-provider interactions and the prediction of potential pathogen spread within healthcare facilities.

Candidates MUST:

Be located in the United States for the current three consecutive years and have the Ability to Obtain a NACI clearance

Job responsibilities include:

• Using an open source patient data generation tool to develop synthetic, time-dependent electronic health records that simulate interactions and evaluation of privacy preserving mechanisms.
• Automating the tracking of all patient-provider interactions within a facility, using EHR data to create a dynamic graph of potential transmission pathways
• Implementing tokenization techniques to enable interaction data analysis while safeguarding patient and provider privacy
• Applying Graph Modeling and Learning Linked Prediction to create predictive model for the spread of infectious diseases
• Applying unsupervised learning techniques in training and refining the predictive model
• Developing advanced analytics to assign risk scores based on predefined risk thresholds
• Leading the development of an alert system based on predefined risk scores and ability to reverse tokenization based on policy and human-in-the-loop oversight

Requirements:

• Master’s degree in Data Science, Statistics, Applied Mathematics, Computer Science, Engineering, Public Health, Epidemiology, Biostatistics, or related disciplines
• 6+ years of experience in data science research
• 3+ years of post-Master experience developing machine learning models
• Expert knowledge in statistical methods, machine learning algorithms, and data visualization techniques.
• Strong working knowledge of synthetic-data generation, preferably in health use cases
• Hands on experience developing predictive model using Graph Neural Network and Learning Link Prediction
• Experience of using tokenization to preserve privacy
• Proficiency in languages like Python, R, or SQL is essential.
• Expert in developing numerical solutions to time-dependent and nonlinear partial differential equations, which are useful for simulating risk score propagation
• 3+ years of experience supporting software development using Docker and AWS
• Familiar with Amazon HealthLake and associated technologies
• A team player with strong leadership, communication and problem solving skills to work effectively with a diverse range of stakeholders, including public health officials, data scientists, healthcare providers, and policymakers
• Demonstrated ability to lead large, interdisciplinary research projects, including managing teams, deliverables, and timelines.
• Understanding of ethical considerations & privacy issues related to AI/ML
• Experience working in an agile development environment

Preferred Requirements:

• PhD in Data Science, Statistics, Applied Mathematics, Computer Science, Engineering, Public Health, Epidemiology, Biostatistics, or related disciplines strongly desired
• Experience working  with PII and PHI data
• Experience working with FHIR based Electronic Health Records system
• Experience with research projects in Health or Public Health setting
• Knowledge of public health infrastructure, policies and regulatory requirements
• Strong working knowledge of Synthea or other open source, synthetic patient generator that models the medical history of synthetic patients
• Publications in scientific journals
• Experience working in a federal agency

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Original Posting Date:

While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.

Pay Range:

The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.