Leidos | Data Scientist / Machine Learning Engineer | Reston, VA | United States | BigDataKB.com | 17 Oct 2022

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Job Location: Reston, VA


Job Description:

Are you looking for your next “great mission” professionally? Do you feel like you have more to give, want to learn new skills and be part of a team with a rewarding mission supporting our Military members and their families? Leidos has the perfect job for you!!

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We are looking for a skilled and adaptable Data Scientist/Machine Learning Engineer to join Leidos supporting our $4.3 billion DOD Healthcare Management System Modernization (DHMSM) program, providing the modernization of Electronic Health Record (EHR) capabilities for the Department of Defense. Leidos, along with core partners Cerner, Accenture, and Henry Schein, will support the DHMSM Program Executive Office (PEO) and the Defense Health Agency in the global deployment of our proposed EHR system that will deliver improved system capability to the DoD whenever and wherever healthcare is required. This is one of the most exciting, cutting-edge programs that you can be a part of with Team Leidos. Our solutions will improve the quality of healthcare for some 10 million military personnel and their families. Let talk about how good it feels when you know you are making that kind of difference!!

As we move toward a digital engineering design and integration approach, the Data Scientist Machine Learning Engineer will play an integral role on the data management team.


The Data Scientist/Machine Learning Engineer role will be essential to building and maintaining a ML-AI assisted data catalog that includes DHMSM concepts, data domains and data elements, and will be responsible for building and maintaining a massive catalog of MHS GENESIS EHR for all registries, and further enriching this data to help EHR integration. A modern data catalog that extracts metadata from a wide range of sources and supports many different data and asset types allows a single tool to serve both data scientists and managers, along with a range of other users. You will use cutting-edge technologies in software engineering, machine learning, deep learning, and analytics to tackle problems ranging from outlier detection of compute workloads, associated data pipelines, run times, visualization and model serving. A data catalog’s fundamental component is the data governance function. This project has the potential to bring intelligence and flow to every part of the data stack and truly act as the gateway for a truly intelligent data management, selected candidate will be expected to embed visualizations into the catalog, creating a living document others can reference in the future.

  • Deploy operational analytics capable of tracking and integrating real-time streams of information, deriving conclusions based on predictive models of behavior, and triggering automatic responses and alerts.

  • Master and help establish the team’s technical stack (centered on Python, Cloud, ML/DL, Kubernetes); understand COTS product configurations, data and pipeline systems

  • Write Python scripts to clean and check data from vendors

  • Research, design, and implement data models and cutting-edge algorithms on high-dimensional, fast-moving, unstructured and structured data

  • Process complicated and large-scale data sets using distributed computing platform; extract insights from data

  • Lead data science projects. Take ownership of whole lifecycle of projects. Communicate with project managers, data science and engineering teams to align progress and collaborate on system integrations.

  • Automatically deduce the owners and experts for data tables or dashboards based on SQL logs

  • Automatically stop downstream pipelines when a data quality issue is detected, and use past records to predict what went wrong and fix it without human intervention

  • Automatically purge low-quality or outdated data products

  • Present on projects within team or beyond to boost understanding, visibility, and business impact

  • Perform and support data management tasks that deliver a complete view of Military Health System (MHS) health care data throughout the enterprise to the analytic community and MHS leadership.

  • Develop solutions to data analytics problem and issues that require depth of technical knowledge and understanding of impact on the end product.

  • Act as a SME of data residing in MHS Data Repository (MDR), MHS Mart (M2), and HealtheAnalytics (HeA).

  • Extract data from multiple data warehousing sources.

  • Ensure data integrity is maintained by completing routine data quality analysis.

  • Develop presentations for leadership to share findings and assist in making actionable recommendations.

As machine learning competency expands, additional tasks will include:

  • Find patterns/clusters in information; providing insight where one would not know to look for

  • Explore data using models: apply statistical analysis and machine learning algorithms against the integrated data.

  • Formalize the development, integration, and use of models to inform enterprise and program decision making

  • The structure of the messages or streams of data that pass between systems and organizations, are not usually persisted, should nevertheless be modeled.

  • Transform the culture to adopt and support digital engineering across the lifecycle

  • Take the Digital Engineering Strategy and articulate it at a level that programs can actually implement.

  • Understand how model data is interconnected with other disciplines/functional areas for consumption.

  • Develop guidance to establish requirements for acquisition of technical data to support product lifecycle activities.

  • Advocate for the tools, technologies, and standards that support technical data management across the product lifecycle.

  • Apply data profiling techniques to a data source (database, file, etc.) to discover the characteristics and features of datasets.

  • Integrate and align data for analysis: Model feasibility depends in part on the source of data. Leverage trusted and credible sources. Apply appropriate data integration and cleansing techniques to increase quality and usefulness of provisioned data sets.

  • Choose data sources: Identify gaps in the current data asset base and find data sources to fill those gaps.

  • Acquire and ingest data sources: obtain data sets and onboard them

  • Explore reinforcement learning techniques based on achieving interagency data interoperability goals


  • BS degree and 8-12 years of prior relevant experience or Masters with 6 10 years of prior relevant experience.

  • US Citizen with ability to obtain ADP2/IT2 Public Trust. Federal Government requirement.

  • Experience in SAS, SQL, Business Objects and Tableau.

  • Experience with Process Modeling and Data Modeling

  • Ability to establish and grow competency with Machine Learning.

  • Knowledge of querying and automating processes to extract, model, integrate, and evaluate complex datasets and business problems for practical application.

  • Extensive experience working with Military Health Systems (MHS) data systems, such as Management Analysis and Reporting Tool (M2)/MHS Data Repository (MDR).

  • Ability to thrive in a fast paced cross-functional environment.

  • Minimum 3 years working with MHS data.

  • Proficient in Microsoft Office products, especially Excel, Word, and PowerPoint.


  • Experience with data management plans.

  • Prior experience working collaboratively with team members/stakeholders, documenting existing processes, creating process models and providing visibility on activity flows, documenting changes visually, helping achieve “buy-in” from clients/stakeholders and/or team members.

Pay Range:

Pay Range $113,100.00 – $174,000.00 – $234,900.00

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.


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