The qualified candidate must have strong communication skills, adept at blending technical skills, analytical expertise, and business acumen. The Marketing Scientist I will contribute to the execution and delivery of strategic analytic and insight projects for RRD’s clients and select internal constituencies. The position will have a broad scope of responsibility, including data transformation, statistical analysis, strategy development, testing, and ROI optimization. He or she will gain experience across a variety of clients and applications and interact with various functional areas (Business Units, Marketing, Technology) and industries. The analyst must be comfortable both performing and presenting analysis at all levels of detail. Responsibilities:Execute analytical projects from established data requirements, that specific client deliverable, tasks, and analysis timing. Acquire and aggregate data from a variety of sources; audit contents and ensure proper usage of data fields; merge, summarize, and transform data as required for analysis. Work with internal teams to address client business problems, set expectations, communicate progress and issues, produce results, and deliver findings to clients in a highly professional manner. Apply statistical methods and execute analyses, such as building targeting models, developing segmentation schemes, creating test designs, measuring program performance, identifying a Qualifications 4 Year / Bachelors Degree-Bachelor's degree in a quantitative discipline is required. Graduate Degree-Higher education in statistics, mathematics, analytic MBA, or other quantitative graduate degree preferred 1 year of experience. Skills and Certifications: A strong understanding of marketing strategy and analysis, campaign targeting, test design, and measurement is strongly preferred. Knowledge of statistical techniques such as logistic regression, cluster analysis, and basic experimental design. Exposure to newer data transformation techniques such as data/text mining, pattern recognition and dataframe operations. Demonstrated strength in effective written and oral communication. Ability to provide presentation of technical/statistical findings to non-technical audiences. Hands-on experience using data query and analytic tools such as SQL, and Python; must be able to work with large databases (querying, joining, summarizing) to create analytic datasets and execute analyses. Familiarity with standard data analytics libraries such as Pandas and Num. Py is a plus. Experience with data visualization (e.g., matplotlib, R), other languages (e.g., Python), command line prompts in Linux and cloud platforms (e.g., AWS) is a plus. Capable of working in a dynamic environment, handling multiple projects, meeting deadlines, able to prioritize appropriately and respond to issues quickly and creatively.