Research Scientist - Data Science, AI & Digital Health
Role details
Job location
Tech stack
Job description
This fifth-level Research Professional serves as a senior individual contributor and recognized content expert in data science, artificial intelligence and digital health research. The position independently leads and advises on highly complex, system-level research projects and portfolios, working with faculty leaders with particular emphasis on aligning study design, analytic strategy, and real-world research operations.
The role functions as a strategic and methodological partner to faculty investigators, providing advanced guidance on when, how, and why data science and digital health methods should be applied-rather than serving as a primary analyst or system implementer. The incumbent integrates across disciplines and roles to translate research aims into executable, data-driven studies that advance learning health system sciences, working under general supervision with the authority to make daily operations decisions., 35%: Data Science & Digital Health Expertise
- Apply advanced data science, computational modeling, and digital health knowledge to shape research questions, hypotheses, and analytic approaches, rather than executing routine analyses.
- Evaluate appropriateness, strengths, and limitations of analytic methods, data sources, and digital tools in relation to study aims and operational constraints.
- Interpret analytic outputs in scientific and operational context, translating results into insights and recommendations for investigators and stakeholders.
- Leverage, rather than replicate, and coordinate the work of biostatisticians, analysts, and informatics staff and faculty.
- Contribute substantively to grant applications and manuscripts as a methodological and design co-author.
25%: Bridging Study Design and Study Operations
- Serve as the primary integrator between faculty vision and operational execution for data-enabled studies. Lead integration of data science and digital health considerations into study design at the concept, proposal, and protocol stages.
- Advise faculty on tradeoffs among rigor, feasibility, scalability, and resource requirements in real-world research environments.
- Ensure coherence among research questions, data availability, analytic plans, and implementation workflows. Anticipate and resolve mismatches between protocol intent and day-to-day research operations.
- Collaborate with study staff, analysts, informatics teams, and health system partners to operationalize complex designs.
- Guide development of reusable workflows and approaches that support multiple studies.
- Support project management primarily or working collaboratively with staff.
15%: People Management
- Provide leadership for staff and foster a professional, diverse and equitable working environment within the center for people from all backgrounds, communities, and identities, where staff are empowered and accountable.
- Directly oversee and manage CLHSS data scientists (2), who will be matrixed to assigned research projects based on skills and expertise, to meet defined project deliverables.
- Develop staff capacity and set goals aligned with overall center strategies.
15%: Strategic Consultation & Portfolio Leadership
- Provide consultative expertise to CLHSS leadership, faculty, and project teams on data science and digital health strategy.
- Shape the Center's approach to data-enabled research by identifying emerging methods, technologies, infrastructure needs, as well as grants and other funding opportunities.
- Influence research direction and quality across data science portfolio (multiple concurrent projects) without formal supervisory authority.
- Represent CLHSS as a subject-matter expert in institutional and cross-unit initiatives.
- Galvanize engagement with data science staff and faculty collaborators through varied approaches to build a pool of expertise supporting active projects, incubation of ideas, and joint pursuit of funding.
10%: Communication, Reporting & Knowledge Translation
- Produce high-level reports, presentations, and guidance documents addressing complex technical and methodological issues.
- Communicate effectively with both technical and non-technical stakeholders.
- Support dissemination of findings through scholarly publications and presentations.
Requirements
- Advanced degree (Master's or higher) in a relevant field (e.g., data science, informatics, public health, biostatistics, or related) with one or more years of research training
- Progressively responsible experience (typically 4+ years) contributing to complex, data-driven health research in academic, clinical, or learning health system environments.
- Demonstrated expertise in applying data science to inform study design, analytic strategy, and research decision-making (e.g., real-world data, computational modeling, EHR-based research, digital tools).
- Experience running data or technology projects of moderate or significant complexity for at least 2 or more years.
- Proven ability to independently lead or influence highly complex research efforts, exercising sound judgment in ambiguous and evolving situations.
- Experience integrating study design with real-world research operations and data environments.
- Strong understanding of health data sources (e.g., EHR, registries, digital health data).
- Demonstrated ability to serve as a consultative partner to faculty and cross-disciplinary teams.
- Track record of scholarly contributions (e.g., grants, protocols, publications). Familiarity with the full research cycle.
- Excellent communication skills, with the ability to translate complex technical and methodological concepts into clear guidance for diverse audiences.
Preferred Qualifications:
- Doctoral degree (PhD, DrPH, ScD, or equivalent) in a relevant field.
- Experience in learning health system research, pragmatic clinical trials, implementation science, or embedded research within healthcare delivery systems.
- Demonstrated success contributing to or securing extramural funding (e.g., NIH, AHRQ, PCORI, foundations), particularly in data-enabled or digitally enabled research.
- Experience working across interdisciplinary teams, including collaboration with biostatisticians, informatics professionals, clinicians, and research operations staff.
- Familiarity with advanced analytic approaches (e.g., machine learning, causal inference, simulation modeling) sufficient to guide method selection and interpretation, even if not the primary analyst.
- Experience guiding development of reusable research infrastructure, such as data pipelines, workflows, or standardized methods across multiple studies.
- Experience mentoring or advising research staff or junior investigators in a non-supervisory capacity.
- Knowledge of regulatory, ethical, and data governance considerations relevant to health data and digital research.
Benefits & conditions
The University offers a comprehensive benefits package that includes:
- Competitive wages, paid holidays, and generous time off
- Continuous learning opportunities through professional training and degree-seeking programs supported by the Regents Tuition Benefit Program
- Low-cost medical, dental, and pharmacy plans
- Healthcare and dependent care flexible spending accounts
- University HSA contributions
- Disability and employer-paid life insurance
- Employee wellbeing program
- Excellent retirement plans with employer contribution
- Public Service Loan Forgiveness (PSLF) opportunity
- Financial counseling services
- Employee Assistance Program with eight sessions of counseling at no cost
While our salary ranges provide a framework, it is important to note that most of the time, the initial pay may not reach the maximum of the range. This approach ensures that compensation reflects the value and unique contributions of each candidate while maintaining equity within our organization. As part of our commitment to fair and equitable compensation, please be aware that the salary offered to incoming candidates will be based on their individual credentials and experience.