Client Data Analyst II

Responsibilities

• Apply advanced statistical methods to analyze complex datasets, identify business drivers, and develop actionable recommendations for clients and internal stakeholders.
• Lead the development and evolution of measurement frameworks, KPIs, and analytical methodologies to evaluate program performance and business outcomes.
• Design, execute, and interpret quantitative research studies, including market testing, A/B testing, multivariate experiments, and quasi-experimental analyses.
• Build and validate statistical models to understand customer behavior, sales performance, promotional effectiveness, and other key business questions.
• Conduct hypothesis testing, regression analysis, forecasting, segmentation, and other advanced analytical techniques to support strategic decision-making.
• Translate analytical findings into compelling business narratives, communicating insights, risks, assumptions, and recommendations to both technical and non-technical audiences.
• Partner with clients and cross-functional teams to identify opportunities for optimization and develop data-driven go-to-market strategies.
• Stay current on analytical best practices, emerging statistical methodologies, consumer insights research, and industry trends.
• Collect, integrate, cleanse, transform, and validate large, complex datasets from multiple sources while ensuring data quality and integrity.
• Develop reproducible analytical workflows and documentation to support ongoing measurement, research, and reporting initiatives.

Requirements

• Bachelor's degree in Statistics, Economics, Mathematics, Data Science, Analytics, Market Research, or a related quantitative field.
• 3-5 years of experience in analytics, consumer insights, market research, data science, or a related quantitative role.
• Demonstrated experience applying statistical methods to solve business problems and influence decision-making.
• Experience designing and analyzing experiments, including A/B testing, test/control methodologies, multivariate testing, or other research designs.
• Strong understanding of statistical concepts including hypothesis testing, confidence intervals, sampling methodologies, significance testing, regression analysis, and model validation.
• Proficiency in Python, R, SPSS, or other statistical software for data analysis, modeling, and research applications.
• Intermediate to advanced SQL skills and experience working with large, complex datasets from multiple sources.
• Experience with predictive analytics, forecasting, segmentation, driver analysis, or other advanced modeling techniques preferred.
• Strong data preparation skills, including data cleaning, transformation, validation, and quality assurance.
• Ability to translate complex analytical findings into actionable business recommendations and communicate results to both technical and non-technical audiences.