Principal AI Data Scientist, Service
United StatesFull-TimePrincipal
Salary150000 - 215000 USD per year
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Job Details
- Experience
- 7+ years
- Required Skills
- PythonSQLPyTorchSnowflakeAzureCI/CDscikit-learnLLM
Requirements
- Advanced degree (Master’s or PhD) in Data Science, Computer Science, Operations Research, Applied Mathematics, or a related quantitative field
- 7+ years of experience in AI, data science, or advanced analytics, ideally within service operations, supply chain, or related domains
- Strong programming skills in Python
- Experience with ML frameworks such as scikit-learn, PyTorch, or similar
- Expertise in optimization techniques and tools (e.g., OR-Tools, Gurobi, or equivalent)
- Proven experience building and deploying production-grade AI/ML solutions in cloud environments (preferably Azure)
- Hands-on experience with modern data platforms such as Snowflake, including data modeling and SQL optimization
- Strong understanding of CI/CD pipelines and deployment workflows for AI systems
- Ability to translate complex technical concepts into business value and operational outcomes
- Experience with service operations systems (ERP, field service platforms) and large language models or agent-based workflows is a plus
- Excellent communication and collaboration skills in cross-functional, global environments
Responsibilities
- Lead the design, development, and deployment of end-to-end AI and data science solutions supporting service operations and supply chain optimization.
- Build, productionize, and maintain machine learning, optimization models, and intelligent workflows in a cloud-native environment.
- Own the full AI lifecycle, including problem framing, data exploration, feature engineering, modeling, deployment, and continuous improvement.
- Collaborate with service operations, field teams, and technical stakeholders to translate business challenges into scalable AI solutions.
- Develop robust data models and pipelines to support analytics, optimization, and real-time decision-making.
- Ensure reliability, scalability, and security of deployed models through monitoring, alerting, and retraining strategies.
- Partner with engineering and platform teams to integrate AI solutions into production systems using modern CI/CD practices.
- Drive innovation by exploring new AI techniques, including advanced optimization and intelligent automation frameworks.
- Support workshops, stakeholder engagement, and solution alignment across global teams.
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