Note: The job is a remote job and is open to candidates in USA. Kreate is a family-owned plastic injection molding company that creates consumer goods for major retailers. The Data Scientist transforms plant, finance, and retail data into analyses, reports, lightweight applications, and automation that support business decision-making. The role partners with plant, finance, engineering, and retail teams to investigate data issues, define metrics, and deliver recurring analytics.
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
Skills
- 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
Benefits
- Remote work
Company Overview