Data Analyst
LMI
full-time
Posted on:
Location Type: Remote
Location: Remote • 🇺🇸 United States
Visit company websiteSalary
💰 $55,256 - $86,095 per year
Job Level
JuniorMid-Level
Tech Stack
PythonSQLTableau
About the role
- Utilize and manage diverse data sets provided by VA’s Office of Mental Health (OMH) and Office of Suicide Prevention (OSP), including Veterans Outcome Assessment (VOA), satisfaction surveys, clinical templates, Northeastern Program Evaluation Center (NEPEC) collected data, and performance measures to answer program evaluation and assessment questions
- Implement data cleansing and manipulation to ensure data quality and integrity
- Conduct statistical analyses and generate analytical data sets as per project requirements
- Run tests to validate the quality, integrity, and statistical validity of data sets
- Maintain and update databases, ensuring data is stored securely and is easily accessible for analysis
- Assist in the development and execution of data analysis methodologies under the guidance of the Lead Quantitative Social Scientist
- Support the independent evaluation of mental health care and suicide prevention programs to measure effectiveness, cost-effectiveness, and patient satisfaction
- Perform comparative analyses to determine program effectiveness across various veteran cohorts, sub-groups, and service gaps
- Identify trends, patterns, and correlations within the data to evaluate program performance
- Assist in the preparation of annual evaluation reports and Congressionally Mandated Reports (CMR)
- Document and organize SAS/SQL/R/Python codes used for data analysis and report generation
- Prepare data-driven reports, visualizations, and presentations to convey analytical findings clearly
- Use tools like Tableau, Power BI, or custom scripts to visualize data in a way that is easy to understand for stakeholders
- Communicate any data inaccuracies or deficiencies to the Lead Quantitative Social Scientist and assist in proposing solutions
- Collaborate with VA program leads, stakeholders, and external partners for data acquisition and methodology review
- Participate in discovery phase activities, including data collection, stakeholder interviews, and transition meetings
- Ensure that all analytical work complies with rigorous research standards
- Support the Lead Quantitative Social Scientist in ensuring that deliverables are accurate, comprehensive, and submitted on time
Requirements
- Bachelor’s degree in Data Science, Statistics, Computer Science, Public Health, or a related field
- At least 2-3 years of experience in data analysis, data management, data mining, machine learning, and statistical methodologies
- Proficiency in statistical analysis software and programming languages such as SAS, SQL, R, and Python
- Experience with developing queries for routine data requests and reports
- Experience with data visualization tools and techniques such as Tableau, Power BI, or similar for creating reports and dashboards
- Strong understanding of data quality assurance and data cleansing processes
- Expertise in building and cleaning databases from multiple data sources, combining cost and performance information
- Demonstrated ability to pay close attention to detail and ensure high levels of data accuracy
- Excellent problem-solving skills with attention to detail
- Strong written and verbal communication skills to effectively convey complex data insights to diverse audiences
- Ability to work independently as needed
- Ability to collaborate with the integrated project team to compile, collate, integrate, and transform large sets of data into simplified, meaningful, and actionable trends and visualizations for non-technical stakeholders
- Ability to manage multiple tasks and meet deadlines effectively
- Ability to pass a government background investigation
Benefits
- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
data analysisdata managementdata miningmachine learningstatistical methodologiesdata cleansingstatistical analysisdatabase managementdata visualizationreport generation
Soft skills
attention to detailproblem-solvingwritten communicationverbal communicationcollaborationindependencetime management