Research Associate/Data Analyst

School: Harvard School of Dental Medicine

Department/Area: Oral Health Policy and Epidemiology

Position Description

We are seeking a research Data Analyst in the Department of Oral Health Policy and Epidemiology (OHPE). This position will work with Dr. Hawazin Elani and her team which undertakes a range of health policy projects focused on disparities in oral health.

Specific responsibilities of this position include manage various datasets, estimate statistical models, report formats and other analysis considerations, determine and write statistical considerations and algorithms for protocol documents according to study design and appropriate statistical methods, manage and maintain documentation of files and analyses. This person will summarize and present findings as reports, tables, graphs, and models, and will collaborate on manuscripts, abstracts, and other publications documenting study findings. The person will be responsible for successful completion of these tasks within competing project timelines, and must ensure integrity of data collection, data review, data compilation, and analysis techniques. The person must follow proper procedures for working with confidential data in compliance with institutional and regulatory guidelines, including staying current with training requirements related to human subject's research and data security.

This job is ideal for someone with enthusiasm for empirical research and the ability and interest to learn new skills and take initiative as projects develop.

Duties and responsibilities include, but are not limited to, the following:

• Data Analysis and Programming
• Develop study questions and analytic approach (including literature searches and data

searches using various sources)

• Optimize, test, and debug model code
• Clearly document model development, structure, logic, coding, testing
• Engage in research dissemination activities including presentation at lab meetings and regular

updates to funding agencies

• Participate in modelling work group meetings and contribute to the development of new

projects and proposals

• Able to work independently, as well as collaboratively with other research team members.
• Abide by and follow the Harvard University IT technical standards, policies and Code of

Conduct

• Analyze electronic health records, administrative claims, and national survey data

Basic Qualifications

• Bachelor's degree in statistics, computer science, data science, or closely related field with 2+ years of relevant experience. Proficiency in statistical programming languages (Python, R, or SQL) is required.
• Experience and knowledge of quantitative methods including causal inference and econometric analyses.

Additional Qualifications

• Master's degree in statistics, economics, or related quantitative field preferred.
• Experience working on public health, health services, and/or epidemiological research projects strongly preferred
• Strong quantitative background, experience working with large data sets and statistical and data management
• Desire to learn new modelling approaches and statistical methods as necessary for the successful completion of research projects
• Previous experience with health care claims (Medicare, Medicaid, and private insurance plans) strongly preferred
• Ability to work both independently and as a team member.
• Excellent communication skills, and experience collaborating effectively on shared research tasks

Special Instructions

Working Conditions: Work is performed in an office setting. Individual hybrid work options for this role will be discussed during the interview process.

Visa sponsorship is not available for this position.

Contact Information

Hawazin Elani
188 Longwood Avenue
Boston, MA 02115

Contact Email: [email protected]

Salary Range

$65,000-$85,000
Compensation for the selected candidate will be determined by several factors, years of experience, relevant training or qualifications, area of education/training/scholarship, and professional achievements.

Minimum Number of References Required: 2

Maximum Number of References Allowed: 4

Keywords

research, oral health, health policy, data, machine learning, statistics