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Robert Half is partnering with an Austin-based client to hire a Senior Data Scientist for a long-term contract. Applicants must have a PhD in Statistics, Economics, or related quantitative discipline.
Job Responsibility:
Lead the design, execution, and interpretation of A/B tests and quasi-experiments to evaluate user migration initiatives, growth marketing efforts, and campaign effectiveness
Partner closely with cross-functional teams across product, engineering, and marketing to embed experimentation into product development and iteration cycles
Serve as a subject-matter expert on experimentation best practices, including hypothesis formulation, metric selection, experimental design, and results interpretation
Apply advanced causal inference techniques when randomized experiments are not feasible, or to inform prioritization and design
Contribute to the development and scaling of centralized experimentation frameworks, tools, and documentation across the organization
Independently extract, transform, and analyze data from complex, large-scale systems using SQL, Python, and related analytics tools
Communicate insights and experimental results clearly and effectively to both technical and non-technical stakeholders to drive informed business decisions
Stay current on emerging methodologies in experimentation, causal analysis, and applied statistics, and bring new ideas into practice
Requirements:
Master’s or PhD in Statistics, Economics, Econometrics, or a related quantitative discipline
5+ years of experience in a data science role with a strong focus on experimentation (3+ years with a PhD)
Deep expertise in A/B testing and causal inference, including quasi-experimental methods
Strong proficiency in SQL for data extraction and transformation
Strong proficiency in Python, including statistical and data science libraries
Broad, applied knowledge of statistical methods and machine learning techniques
Demonstrated ability to influence product and business decisions through clear, actionable insights
Experience contributing to or developing experimentation frameworks, best practices, or internal analytics tooling