Client Data Analyst II

Acosta•Dallas, TX

1d•$80,000 - $85,000

About The Position

The Client Data Analyst II partners with clients and internal teams to advance initiatives through strategy, analytics, and actionable insights. The role optimizes program measurement to support field team success, uses business- and market-relevant information to inform decisions and adapt tactics, and helps evolve and grow the data-driven discipline within supported programs. The analyst applies advanced statistical methods to complex datasets; develops measurement frameworks, KPIs, and analytical methodologies; designs and evaluates quantitative research; and builds statistical models to address business questions. The position also involves preparing and validating data from multiple sources, creating reproducible analytical workflows, communicating findings to technical and non-technical audiences, and collaborating with clients and cross-functional teams on optimization and go-to-market strategies. Participation in client meetings, internal strategy sessions, and occasional field ride-alongs helps maintain understanding of program operations and objectives. Mosaic describes its values as people minded, integrity, results oriented, trust, teamwork, innovation, and balance. Mosaic North America is an Equal Opportunity Employer and states that it will work with applicants who require disability accommodations to meet their needs to the extent required by law.

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 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.
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 technical and non-technical audiences.
Experience developing measurement frameworks, KPIs, and analytical methodologies to evaluate business performance.
Strong analytical thinking, intellectual curiosity, and problem-solving skills, including the ability to identify meaningful insights in large datasets.
Experience managing multiple projects and delivering high-quality work in a fast-paced and often ambiguous environment.
Intermediate to advanced Excel skills, including formulas, pivot tables, and data analysis functions.
Willingness to learn the business, understand client objectives, and identify opportunities for measurement, analytics, and strategic insights.

Nice To Haves

Experience with predictive analytics, forecasting, segmentation, driver analysis, or other advanced modeling techniques.
Experience with Power BI, Tableau, Looker, or other visualization tools for communicating insights and analytical results.
Remain informed on emerging analytical methodologies, market research techniques, consumer behavior trends, and industry best practices.

Responsibilities

Apply advanced statistical methods to 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 business questions.
Conduct hypothesis testing, regression analysis, forecasting, segmentation, and other advanced analytical techniques to support strategic decision-making.
Translate analytical findings into business narratives, communicating insights, risks, assumptions, and recommendations to technical and non-technical audiences.
Partner with clients and cross-functional teams to identify optimization opportunities 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 measurement, research, and reporting initiatives.
Participate in client meetings, internal strategy sessions, and occasional field ride-alongs to understand program operations and business objectives.
Use statistical and analytical software such as Python, R, SQL, and visualization tools to conduct analyses and communicate results.