Data Scientist
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Job Description
Data Scientist turning manufacturing, retail, and finance data into actionable analytics for Kreate, a family-owned plastic injection molding company. Building Python tools, reporting, and automation for plant and business decisions.
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
- Present analytical findings and business insights to plant leadership and finance teams
- Collaborate with cross-functional stakeholders to define metrics, understand business requirements, and translate analytical findings into practical business decisions
- Partner closely with plant managers, finance, engineering, and retail teams
- Report to the VP of AI & Analytics
Requirements:
- 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
- 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
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