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

Curate Partners LLC; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

Curate Partners LLC seeks a data science professional to design experiments, apply causal-inference methods, and lead analytics in a fast-moving, cross-functional environment.

You will analyze A/B tests, develop predictive models, validate data, and present findings to product and execution teams, with emphasis on rigorous methods and practical decisions.

Qualifications include advanced degree in a quantitative field, strong SQL/Python skills, and experience with real-world datasets.

Qualifications

• Advanced degree in Statistics, Data Science, Economics, Mathematics, Computer Science, or 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.

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.
• 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.
• Present findings and recommendations clearly to technical and non-technical audiences.
• Adapt quickly as business priorities, program designs, or available data change.

Skills

Statistical analysis

Experimental design

Causal inference

Analytical thinking

Communication of methods

Education

Advanced degree in Statistics, Data Science, Economics, Mathematics, Computer Science, or related quantitative field

Tools

SQL

Python

BigQuery

Job description

• 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
• 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.

Applied Data Science and Analytics

• 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.

Cross-Functional Leadership

• 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.

Solving and Analytical Judgment

• 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.