About Us

DLH delivers improved health and national security readiness solutions for federal programs through science research and development, systems engineering and integration, and digital transformation. Our experts in public health, performance evaluation, and health operations solve the complex problems faced by civilian and military customers alike by leveraging advanced tools – including digital transformation, artificial intelligence, data analytics, cloud enablement, modeling, simulation, and more. With over 3,200 employees dedicated to the idea that “Your Mission is Our Passion”, DLH brings a unique combination of government sector experience, proven methodology, and unwavering commitment to innovation to improve the lives of millions.

Overview

DLH is seeking an entry level Data Analyst I to serve on a multi-disciplinary team that conducts data linkages and analyses and reports on findings from clinical-and population-based epidemiological studies on behalf of the federal government. These studies focus on the impact of the physical and social environment on human health and well-being, as well as genetic, occupational, and lifestyle risk factors. This position is part of a multi-disciplinary team that includes data managers, analysts, and operations staff. This position offers opportunities for professional development, innovation, and collaboration across disciplines.

The Data Analyst:

Utilizes R and/or SAS to gather, analyze, and organize data, aiding clients in making informed decisions. Manages master data, contributing to its creation, updates, and removal. Ensures the quality of imported data, collaborating with QA analysts when needed. Handles data set commissioning and decommissioning. Processes confidential data as per project guidelines. Assists in report and analysis development. Contributes to design and management of the reporting environment, encompassing data sources, security, and metadata. Supports the data warehouse in refining reporting requirements and enhancing data integrity. Produces reports from various systems, troubleshooting and reporting on database issues. Evaluates changes to source production systems. Conducts end-user training on new reports and dashboards. Offers technical insight on data storage, mining, and cleansing techniques. Evaluates existing tools and databases, providing software recommendations. Familiar with methodologies for managing extensive databases. Shows proficiency in managing extensive datasets along with expertise in adhering to metadata standards.

Responsibilities

Data Processing and Analysis:

• Utilize advanced R and/or SAS for data extraction, cleaning, transformation, and loading (ETL) processes
• A strong command and a good understanding of the use of SAS Arrays, Macros, Proc SQL, and/or {dplyr/tidyverse} packages in R are necessary to extract, transform, clean, and load study data successfully. 
• Experience merging, stacking, and transforming data from long to wide or vice versa using either R or SAS (if not both) is needed.
• Perform statistical analysis and data modeling to extract actionable insights under the direction of senior analysts and project leadership.
• Develop and implement algorithms for data analysis and reporting in collaboration with senior analysts and project leadership.
• Analyze trends or patterns in complex data sets, isolate anomalies, and report findings to leadership.

Geospatial Data Management with R: Familiarity with key R packages for handling geospatial data such as:

• {sf}: simple features R package for working with vector data: point, line and polygon.
• {ggplot2}: data visualization R package for creating graphics and maps

Database Management:

• Design and manage databases using R and/or SAS tools.
• Ensure the efficient organization and storage of large datasets.
• Collaborate with IT teams to optimize database performance.
• Acquire data from primary or secondary data sources and maintain databases/data systems.

Data Visualization:

• Create interactive and insightful data visualizations using SAS and/or R packages, i.e., R ggplot2.

Quality Assurance:

• Implement quality control processes to ensure data accuracy and integrity.
• Conduct validation checks and troubleshoot data discrepancies.

Collaboration:

• Work closely with epidemiologists, biostatisticians, data scientists, analysts, and other stakeholders to understand analytical requirements.
• Collaborate with cross-functional teams to integrate data-driven solutions into business processes.

Documentation:

• Maintain detailed documentation of SAS and/or R code, data processes, and analyses.
• Maintain detailed documentation of the data dictionary, code book and structure for other users to easily understand data fields.

Version Control Systems

Proficiency with version control systems like Git, GitLab/GitHub for collaborative geospatial projects and data analysis.

Software:

• SAS – Base SAS, Advanced SAS (Proc SQL, SAS Macros, Arrays)
• R – {tidyverse} R package, especially the {dplyr} and {ggplot2} libraries for data manipulation and visualization
• {sf} for geospatial analysis

Qualifications

Education:

BS in Mathematics, Economics, Computer Science, Information Management or Statistics or equivalent.

Experience:

2 years minimum relevant work experience.

• Proven experience managing Survey/Government/Electronic Health Record data using SAS and/or R.
• Proficiency in statistical programming and data manipulation with SAS and/or R.
• Strong knowledge of data management principles and best practices.
• Experience in database management and optimization.
• Excellent analytical and problem-solving skills.
• Effective communication and collaboration skills.
• Detail-oriented with a focus on data accuracy.
• Demonstrated ability to work independently and in a team environment.
• Adept at data queries, geospatial analysis, report writing, and presenting findings.
• Experience handling publicly available survey data is a plus: National Health and Nutrition Examination Survey (NHANES), American Community Survey (ACS), and National Health Interview Study (NHIS).