Data Scientist

As the Data Scientist, your responsibilities will include:

Role Overview

Lead data science initiatives within the Digital Operations team, driving innovation and operational excellence across Supply Chain, Manufacturing, and Quality.

Manage and mentor citizen data scientists while spearheading complex data science projects from inception to deployment, ensuring continuous alignment with business objectives.

Leadership and Project Management

Lead and manage citizen data scientists through all phases of data science projects, providing mentorship and guidance.

Collaborate with cross-functional teams to design, scope, and prioritize data science projects, including defining data collection methods, modeling techniques, and expected outcomes.

Manage data science projects from initiation to completion, continuously revisiting and refining models to ensure they meet evolving business needs.

Develop and maintain governance principles for data science projects, ensuring best practices are followed.

Problem Analysis and Project Management

Develop advanced analytics models (predictive, prescriptive, machine learning, simulation, statistical) to address complex business challenges in Supply Chain, Manufacturing, and Quality.

Utilize advanced machine learning and statistical techniques to perform data discovery, uncovering new correlations and interdependencies among variables that can drive operational improvements.

Lead the development of scalable data pipelines and integrate model performance management tools into existing business infrastructure.

Data Collection, Exploration, and Integration

Access and integrate data from various sources, including SQL databases and other systems relevant to Digital Operations.

Conduct data cleaning, transformation, and preprocessing to ensure data quality and readiness for modeling.

Apply statistical analysis, such as hierarchical clustering and PCA, to uncover insights.

Aid in the creation of data pipelines for more efficient and repeatable data science projects

Apply statistical analysis and visualization techniques to various data, such as hierarchical clustering and principal components analysis (PCA)

Develop classification and regression models or apply unsupervised learning to aid in uncovering insights in data.

Operationalization

Develop advanced analytics models (predictive, prescriptive, machine learning, simulation, statistical) to address complex business challenges in Supply Chain, Manufacturing, and Quality.

Utilize advanced machine learning and statistical techniques to perform data discovery, uncovering new correlations and interdependencies among variables that can drive operational improvements.

Lead the development of scalable data pipelines and integrate model performance management tools into existing business infrastructure.

Collaborate with IT and data engineering teams to deploy and monitor production ML models, establishing best practices around ML production infrastructure.

Continuously monitor, revisit, and refine deployed models to ensure they remain aligned with business objectives and adapt to changing operational needs.

Work closely with stakeholders to communicate findings, insights, and recommendations effectively, ensuring that data-driven strategies are integrated into decision-making processes.

Other

Stay updated with the latest advancements in data science, machine learning, and AI, and proactively integrate these innovations into projects where applicable.

Drive strategic initiatives by identifying and prioritizing AI/ML opportunities that align with the organization’s operational goals, challenging the status quo, and promoting bold, innovative thinking.

The Data Scientist position is well-suited for you if you:

Can translate business strategies into actionable plans by leveraging market and functional knowledge

Champion new processes, remove barriers, and adapt current processes to facilitate improvement

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