Oral Care Consumer Data Science and Analytics

Key Skills

Java Programming, Python Programming, SQL, Scrum Agile Methodology, Data science techniques, R Programming, Statistics, JMP, Neural Networks, SAS, Data Lake, Data Analysis

Job Description

We employ nearly 7,000 Research & Development (R&D) employees in 11 countries around the globe. We have state-of-the-art innovation centers and more depth and breadth in science and technology resources than any consumer products company in the world, resulting in over 55,000 patents and applications globally. Our science, Scientists, Engineers, and Researchers consistently receive numerous prestigious awards.

The Ideal Candidate

The successful candidate will join the best-in-class group of scientists to grow and advance science and capability development in Oral Care R&D. The ideal candidate will have a PhD in Statistics/Data Science with relevant and practical hands-on experience in consumer research methodology. The individual must have a proven track record of technical problem-solving skills, experience with relevant methods, instruments, software, and ability to bring forward the state-of-the art statistical tools and approaches. The individual’s ability to condense complex information and complex data analysis into simpler models will be a critical aspect of consideration along with the ability to work in teams where statistical experience may be widely variable.

The individual will work in the Health Care Statistics and Data Management section of the Measurement Sciences organization as a unique interface between technology development, formula design, and consumer acceptance of our products. The role will focus on design and analysis of consumer research studies, upskilling the quantitative skills of the organization, development of statistical methods and models in support of Oral Care programs and holistic integration of such methods into the overall technology development and consumer delight processes.

This position also requires effective collaboration and communication skills with multifunctional teams, ability to explain complex statistical concepts to non-experts, and collaboration with a multitude of diverse subject matter experts (formulators, process and data engineers, chemists, clinicians, etc.). This role is critical for improvement of global oral health by delivering superior products of superior quality, and the work directly translates to new technology identification, product development and commercialization for all products in the Oral Care portfolio. This is a unique opportunity to be part of a fast-moving and agile global team to grow and shape the future of Oral Care.

IN THIS ROLE YOU WILL:

• Provide leading edge statistical and data science expertise for the Oral Care products R&D organization to guide project direction and compliment consumer understanding
• Provide statistical expertise for experimental design, analysis, interpretation and reporting of consumer research studies
• Apply statistical and data science expertise to claims and credentialing support, predictive modeling, image/video analysis, automating analysis when appropriate, and upskilling the quantitative skills of the organization
• Provide exploratory data analysis of the consumer data lake and other sources of data to extract maximum value from existing data
• Leverage advanced statistical and data science methods to integrate learnings in consumer, sensory and business analytics and activate data to drive business decisions
• Generate and apply end-to-end models to drive activation of learnings to the business in partnership with a Consumer Products Research Scientist
• Work on a wide variety of projects with various teams where each project could be vastly different all at the same time

Qualifications

PhD in Statistics / Data Science with relevant and practical hands-on experience in consumer research methodology (ex. Multivariate such as clustering, discrete choice such as Conjoint and MaxDiff, Random effects mixed models for crossover studies, Neural and Convolutional Neural Networks, correlated regression techniques, etc). Experience in using various Statistical/Data Science software packages (ex.R, SAS, Python, JMP)