The Data Scientist turns plant, finance, and retail data into actionable analyses, reporting, and tools that help teams run the business. The role spans manufacturing operations, retail performance, and financial reporting, combining data analysis with lightweight applications and automation. This position partners closely with plant managers, finance, engineering, and retail teams to identify trends, investigate data issues, and improve decision-making. The Data Scientist also owns a portfolio of recurring analytics deliverables and serves as a trusted resource when business metrics need to be understood or validated.

Essential Functions and Responsibilities

• Build and maintain operations analytics covering production, OEE, downtime, scrap, and labor as a percentage of production sales value.
• Analyze retail performance with retail partners, including sell-in vs. sell-through, fill rate, on-time compliance, new-store stocking, and price tests.
• Own recurring analytics deliverables, including daily briefs, weekly operating reviews, and month-end analyses, and automate these processes where appropriate.
• Build reports and lightweight Python web applications that enable business users to access and explore data independently.
• Investigate data quality issues by tracing unexpected results back to their source systems and partnering with engineering to resolve issues upstream rather than applying downstream workarounds.
• Present analytical findings and business insights to plant leadership and finance teams in clear, accessible language.
• Collaborate with cross-functional stakeholders to define metrics, understand business requirements, and translate analytical findings into practical business decisions.

Required Qualifications

• 2–4 years of experience in analytics, data science, or a related field.
• Strong SQL and Python skills, including experience with pandas and statistical or machine learning libraries such as stats models or scikit-learn.
• Sound understanding of statistical concepts, including distributions, regression, statistical significance, and appropriate sample sizes and limitations.
• Experience with Power BI, including DAX, or a comparable business intelligence and reporting platform.
• Ability to clearly explain metric definitions, analytical findings, and data limitations to both operational leaders and executive stakeholders.
• Strong problem-solving skills and the ability to investigate data issues from business outputs back to underlying source systems.

Preferred Qualifications

• Experience with manufacturing KPIs such as OEE, scrap, and cycle time.
• Experience in plastics manufacturing or injection molding.
• Familiarity with retail vendor data, including retailer portals, point-of-sale data, and EDI.
• Experience with time-series forecasting.
• Experience with Flask or a similar Python web framework.
• Experience integrating LLM APIs into analytics tools and applications.

Company Details:

• Location: Remote
• This position will report to the VP of AI & Analytics

Kreate is an equal opportunity employer. The Statements used herein are intended to describe the general nature and level of the work being performed by an employee in this position and are not intended to be construed as an exhaustive list of responsibilities, duties and skills required. Furthermore, they do not establish a contract for employment and are subject to change at the discretion of the Company.