✨ AI Summary
This Principal Data Scientist role focuses on leveraging advanced experimental design, statistics, and machine learning techniques to analyze diverse data sources and generate actionable insights for client services, product enhancement, and business impact within supply chain operations. The position involves training, deploying, and monitoring machine learning models, architecting solutions for the model lifecycle, and optimizing prompts. Responsibilities include summarizing and interpreting data, making recommendations to stakeholders, implementing Extract, Transform, Load (ETL) for data pipelines with a focus on security and privacy, and developing efficient, scalable code. The role requires staying current with data science developments and integrating new knowledge into model development.
Key qualifications include 11+ years of experience in data science, machine learning, or data engineering, or equivalent educational background with relevant experience. Strong proficiency in SQL and Python, experience with ETL/ELT pipelines, data modeling, cloud-based data platforms, AI/ML techniques, and business intelligence tools are essential. The role also emphasizes strong analytical, problem-solving, and communication skills, with the ability to lead technical initiatives and influence stakeholders. Experience in supply chain, manufacturing, logistics, or cloud infrastructure operations is preferred.
🎁 Benefits & Perks
flexible medical, life insurance, and retirement options, volunteer programs
Requirements
Requires 11+ years of experience in data science, machine learning, or data engineering, or a Bachelor's degree with 7+ years of experience, Master's with 5+ years, or Doctorate with 3+ years. Must have 2+ years of experience in production-grade software development. Key technical skills include strong proficiency in SQL and Python, experience with ETL/ELT pipelines, data modeling, orchestration frameworks, cloud-based data platforms, AI/ML techniques, predictive analytics, automation frameworks, and business intelligence/visualization tools. Understanding of forecasting, inventory management, manufacturing, logistics, and supply planning is essential. Strong analytical, problem-solving, and communication skills are required.
Description
Uses advanced experimental design, statistics, technologies, and (e.g., machine learning, natural language processing) to discover and understand data patterns and trends to generate actionable insights and solutions for client services, product enhancement, and business impact. Engages in training, deploying, and monitoring machine learning models, and in architecting solutions for the entire model lifecycle. Optimizes prompts. Summarizes and interprets data and analysis insights and findings to make recommendations to stakeholders. Implements Extract, Transform, Load (ETL) for data pipelines while ensuring data security and privacy. Develops efficient and scalable code and tests. Maintains familiarity with current developments in the data science field and integrates knowledge into model development.