Research Scientist 2

The Research Scientist 2 applies statistical, epidemiologic, and analytical methods to healthcare data to evaluate clinical program performance, understand drivers of outcomes, and support clinical and business decision-making.

This role will be part of the Quality & Cost Strategy Analytics team supporting the performance management and evaluation of clinical programs. The Research Scientist 2 will design and execute rigorous outcomes studies, conduct deep-dive analyses to diagnose program performance, and translate complex analytical findings into clear recommendations for clinical, operational, and business partners.

Responsibilities:

• Design and execute outcomes studies to evaluate the clinical, utilization, and financial performance of clinical programs using observational and experimental methods.
• Apply causal inference, epidemiologic, and statistical methods to estimate program impact and generate credible evidence for decision-making.
• Conduct deep-dive analyses using medical and pharmacy claims, enrollment, and operational data to identify key performance drivers, diagnose performance gaps, and uncover opportunities for improvement.
• Translate clinical and business questions into clearly defined analytical questions and hypotheses, partnering with senior scientists as needed on more complex study designs and methodologies.
• Partner with clinical, operational, financial, and business stakeholders to translate analytical insights into recommendations that inform clinical strategy, program design, contracting, and performance improvement.
• Develop high-quality analytical reports, presentations, and executive-level readouts for internal and external stakeholders.
• Translate complex methodologies and findings into clear, actionable business insights, communicating both implications and analytical limitations to technical and non-technical audiences.
• Independently determine appropriate analytical approaches and make sound methodological decisions in complex or ambiguous situations.
• Manage multiple analytical priorities while maintaining high standards for methodological rigor, accuracy, and reproducibility.

Required Qualifications:

• Master’s degree or PhD in Epidemiology, Biostatistics, Economics, Statistics, Health Services Research, Clinical Informatics, or a related quantitative discipline.
• 2+ years of experience applying statistical, epidemiologic, econometric, or related quantitative methods to large, complex datasets.
• Strong foundation in causal inference and study design, including observational studies, comparative effectiveness research, and randomized controlled trials.
• Experience manipulating and analyzing data using Python, PySpark, R, SAS, or comparable analytical tools.
• Strong analytical thinking and structured problem-solving skills, with the ability to translate clinical and business questions into analytical approaches.
• Ability to select and apply appropriate analytical methodologies to moderately complex research and business questions, with guidance on more complex methodological challenges.
• Excellent written and verbal communication skills, including the ability to synthesize complex methodologies and findings into clear insights and recommendations for non-technical audiences.
• Strong attention to detail and organizational skills, with the ability to manage multiple priorities effectively.
• Demonstrated ability to work independently while collaborating effectively with multidisciplinary and cross-functional teams.
• Strong interest in healthcare and a passion for improving health outcomes and healthcare value.

Preferred Qualifications:

• Experience working in health insurance, managed care, healthcare analytics, or health services research.
• Strong understanding of healthcare payer economics, including healthcare utilization, medical cost, and total cost of care.
• Experience working with medical claims, pharmacy claims, laboratory data, eligibility/enrollment data, and clinical program operational data.
• Familiarity with clinical concepts across chronic and specialty conditions, such as cardiology, chronic kidney disease, oncology, or other high-cost clinical conditions.
• Experience evaluating clinical programs, care management interventions, or value-based healthcare initiatives.
• Familiarity with advanced causal inference and causal machine learning techniques, such as double/debiased machine learning, doubly robust estimation, and meta-learners.
• Proficiency in Python and PySpark, with experience working in Databricks and Microsoft Azure.

Benefits:

• Medical, dental, and vision benefits
• 401(k) retirement savings plan
• Time off (including paid time off, company and personal holidays, paid parental and caregiver leave)
• Short-term and long-term disability
• Life insurance