Data Scientist

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop and sustain operational analytics related to production, OEE, downtime, scrap, and labor as a proportion of production sales value.

• Evaluate retail performance alongside retail partners, focusing on sell-in versus sell-through, fill rate, on-time compliance, new-store stocking, and pricing tests.

• Take ownership of recurring analytics outputs, including daily briefs, weekly operational reviews, and month-end analyses, while automating these processes as needed.

• Create reports and lightweight Python web applications that empower business users to independently access and explore data.

• Examine data quality challenges by tracing unexpected results back to their source systems and collaborating with engineering to address issues upstream.

• Convey analytical insights and business findings to plant leadership and finance teams.

• Work in partnership with cross-functional stakeholders to define metrics, comprehend business requirements, and convert analytical insights into actionable business decisions.

• Collaborate closely with plant managers, finance, engineering, and retail teams.

• Report directly to the VP of AI & Analytics.

⛳️ Requirements

• 2–4 years of experience in analytics, data science, or a related discipline.

• Proficient in SQL and Python, with experience in pandas and statistical or machine learning libraries such as statsmodels or scikit-learn.

• Solid grasp of statistical principles, including distributions, regression, statistical significance, and the appropriate sample sizes and limitations.

• Familiarity with Power BI, including DAX, or a similar business intelligence and reporting tool.

• Capability to clearly articulate metric definitions, analytical insights, and data limitations to both operational leaders and executive stakeholders.

• Strong problem-solving abilities and the capacity to investigate data issues from business outputs back to the underlying source systems.

• Experience with manufacturing KPIs such as OEE, scrap, and cycle time.

• Background in plastics manufacturing or injection molding.

• Knowledge of retail vendor data, including retailer portals, point-of-sale information, and EDI.

• Experience with time-series forecasting.

• Familiarity with Flask or a similar Python web framework.

• Experience integrating LLM APIs into analytics tools and applications.

🏝️ Benefits

• Competitive salary and performance-based incentives.

• Comprehensive health, dental, and vision insurance.

• Generous paid time off and holiday schedule.

• Opportunities for professional development and career growth.

• Collaborative and innovative work environment.