The Data Scientist Lead advises business leaders across the organization on the strategic direction of our Data Science project efforts and infrastructure to capture the greatest opportunity to Mosaic. Role is an expert at identifying causal inference from structured and unstructured data of our operations and will utilize various supervised, unsupervised, and ensemble learning techniques to draw insight and gain predictive power to mission critical KPI’s that drive business objectives. The Lead DS will work with IT to provision the necessary infrastructure to conduct our Data Science experiments and will provide insight into our third-party partnerships, and they leverage their relationships with our partners to lead the frontier of technology enablement that feed proof of concepts use cases. Lastly, they serve as a key mentor to all Data Scientists.
JOB ESSENTIAL FUNCTIONS
Lead New Product Development’s data management program and overall strategy.
Develop, maintain, and interact with Mosaic’s Global Agronomy Database.
Collaborate with fellow team members to create comprehensive analytical solutions from data intake through report-out.
Review, define, and deploy new process improvement opportunities within and between R&D departments.
Identify collaborative data analytical partnerships within or outside of Mosaic that may accelerate our analytical processes.
Oversee North American lab, greenhouse, and field data analytics.
Management of data intake, standardization, analysis, and archival.
Lead statistical experimental design and power analysis activities.
Utilize algorithms, spatial analysis, or other advanced data analytical techniques to identify trends or patterns.
Specific emphasis will be placed on using various visualization or report-out techniques that lead to making critical business decisions.
Use of effective written, oral, and presentation skills to share key findings to commercial, strategy and growth, and senior leadership is required.
Utilize expertise in statistics and databases to support NPD, APT, PRI, and other teams at Mosaic.
Conduct knowledge sharing sessions with peers to ensure effective data use. Continuing learning/education is encouraged to integrate novel analytical techniques and programs to further improve data strategy.
EDUCATION
Master's degree in Engineering, Data Science, or Statistics
Doctorate's degree preferred.
OTHER PREFERRED SKILLS
Understanding of statistical analysis in agricultural or biological systems. Broad knowledge of crop nutrition and soil science
Strong business acumen
Familiarity with geospatial analysis
EXPERIENCE REQUIRED
Minimum of 5 years of applied data analytics and/or data management experience is required.
Experience in agriculture or applied agronomy.
KNOWLEDGE, SKILLS, ABILITIES
REQUIRED
Deep understanding of data strategy, data analysis, and
Understand the basic principles of relationship databases and how they interact.
Applied experience using a variety of data analysis programs is required.
Strong ability to manage relationships within or outside of the organization to complete any organizational data management initiative.
Needs to be very creative, highly motivated and can apply previous experiences to novel solutions.
Willing/able to travel to within Canada and the United States.
Excellent quantitative analytical abilities.
Ability to work independently while managing several projects.
Excellent presentation and verbal communication skills through various multi-media outlets.
Demonstrated critical thinking and decision-making skills.
Ability to work collaboratively with people for various international backgrounds.
Ability to adapt to continually changing business and work environment.
Provide direction, guidance, challenge, and inspiration to department associates as well, help develop the research skills of colleagues.
Strong interpersonal and relationship-building skills.
Ability to effectively work with employees at all levels within the organization.
Ability to quickly synthesize research data from various sources and deliver key insights and recommendations.
