Research Associate/Data Analyst

Research Associate/Data Analyst

Title - Research Associate/Data Analyst

School - Harvard School of Dental Medicine

Department/Area - The Initiative to Integrate Oral Health and Medicine

Position Description - The Initiative to Integrate Oral Health and Medicine seeks a research analyst to join our research team and provide quantitative and analytical support for research projects evaluating the impact of health policies and healthcare delivery models on health outcomes. Our research occupies the intersection of oral health and systemic disease, addressing a historically underrepresented area of health policy with significant implications for health care outcomes, access, and costs. Our research uses large-scale administrative claims, electronic health records, national survey data, and causal inferential approaches to generate evidence that informs health policy and promotes access to care. The selected candidate will work closely with a multidisciplinary team, including clinicians, statisticians, decision scientists, and health services researchers. The individual will contribute to all phases of the research process, including study design, data management, statistical analysis, interpretation of findings, manuscript preparation, and grant development. This position is ideal for an individual who enjoys solving complex data problems, critically evaluating analytic approaches, and contributing intellectually to scientific research.

Under the supervision of the principal investigator, duties and responsibilities include but are not limited to:

• Collaborate on study design, development of analytic plans, statistical analyses, interpretation of results, and preparation of manuscripts and scientific reports.
• Manage, clean, validate, and analyze large healthcare datasets, including electronic health records, administrative claims, and national survey data.
• Develop reproducible workflows for data processing, quality control, statistical analysis, and documentation.
• Critically evaluate data quality, analytic assumptions, and unexpected findings; conduct sensitivity analyses and propose solutions to methodological challenges.
• Participate in the development and implementation of analytic models that apply econometric approaches supporting causal inference to evaluate the clinical, economic, and equity impacts of health policy interventions.
• Support the preparation and drafting of research protocols through literature reviews and identifying required data elements.
• Work independently to identify and troubleshoot analytical issues, communicate methodological decisions and findings clearly to the research team, and contribute to discussions regarding interpretation and next steps.

Applicants can expect to be included as co-authors on all manuscripts resulting from their substantive contributions, with the opportunity for first authorship based on satisfactory progression and leadership contributions.

This is a hybrid remote/in-person position, with candidates expected to reside in the greater Boston area due to some data access restrictions.

This is an annual term position reviewed each academic year on or before June 30th, with extension anticipated through continued funding. We anticipate that a successful analyst will stay with the group for many years.

Basic Qualifications - Master's degree in statistics, biostatistics, epidemiology, data science, computer science, or closely related field; individuals holding a bachelor's degree with substantial prior experience may be considered. Proficiency in statistical programming languages (R, SQL, Stata, or Python) is required. Prior experience with causal inference methods (eg. difference-in-differences, regressiondiscontinuity, instrumental variable analysis) and analysis of large healthcare datasets is strongly preferred. Prior experience with Virtual Research Data Center access and use of Transformed Medicaid Statistical Information System or Medicare analytic files is preferred.

Salary Range - $75,000-$130,000 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