Senior Data Scientist (Causal-inference, A/B Testing, Healthcare)

Curate Partners is seeking a Senior Data Scientist to support program evaluation, causal inference, experimentation, and applied data science. The role involves designing and analyzing experiments, applying quasi-experimental methods, developing analytical datasets and models, validating findings, and communicating recommendations to cross-functional stakeholders.

Responsibilities:

• Design and analyze experiments, including A/B tests and randomized controlled trials when systems and business conditions support them
• Apply causal-inference methods when randomized experimentation is not feasible, including:
• Difference-in-differences
• Synthetic controls
• Propensity-score methods
• Natural experiments
• Quasi-experimental designs
• Carefully structured pre/post analyses
• Select the strongest practical methodology available based on the data, systems, timing, and business constraints
• Clearly explain the strengths, limitations, assumptions, and tradeoffs associated with the selected approach
• Maintain analytical rigor without rejecting useful methods simply because the ideal experimental design is unavailable
• Conduct exploratory data analysis to identify trends, patterns, anomalies, and opportunities
• Use SQL and Python to manipulate data, build analytical datasets, and perform complex statistical analyses
• Develop predictive or machine-learning models when appropriate to the business problem
• Evaluate campaigns, customer communications, product initiatives, and operational interventions
• Validate data and analytical outputs before findings are used in business decisions
• Work directly with product managers, execution teams, analysts, and business stakeholders in production environments
• Help teams define measurable objectives and practical evaluation strategies
• Drive analytical thinking throughout program development and implementation
• Present findings and recommendations clearly to technical and non-technical audiences
• Adapt quickly as business priorities, program designs, or available data change
• Break ambiguous business problems into structured analytical questions
• Consider multiple methods rather than defaulting automatically to one model or experimental design
• Identify when results require additional validation or when the available evidence does not support a definitive conclusion
• Use AI-assisted tools thoughtfully while independently validating their output and underlying logic
• Show the work behind key analytical conclusions

Requirements:

• Advanced degree in Statistics, Data Science, Economics, Mathematics, Computer Science, or a related quantitative field, or equivalent applied experience
• Strong practical experience in statistical analysis, experimental design, and causal inference
• Proficiency in SQL and Python
• Experience working with complex, imperfect, real-world datasets
• Demonstrated ability to design credible analyses when randomized experimentation is unavailable
• Strong understanding of statistical methods and machine-learning concepts
• Excellent problem-solving skills and analytical judgment
• Ability to communicate methods, limitations, and recommendations clearly
• Comfort operating in a fast-moving, cross-functional production environment
• Experience evaluating product, marketing, customer-engagement, healthcare, or operational programs
• Experience with Google Cloud, BigQuery, or comparable cloud-data environments
• Experience in healthcare, insurance, or pharmacy benefits
• Experience working directly with product and execution teams
• Familiarity with production machine-learning workflows