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Key job responsibilities
- Design and implement sophisticated statistical and machine learning models to solve complex supply chain problems
- Partner with BIEs to define data requirements and ensure optimal data architecture for model development
- Apply a range of data science methodologies to conduct analysis for cases where solution approaches are unclear
- Develop and validate hypotheses through rigorous statistical testing and experimentation
- Create scalable algorithms that can be deployed across our fulfillment network
- Build predictive models to optimize operational decision-making
- Communicate complex analytical findings to technical and non-technical stakeholders
A day in the life
Your morning might begin collaborating with your BIE partner to define data requirements for a new network optimization model. You'll then develop and test statistical approaches for identifying operational inefficiencies, working closely with business stakeholders to validate your findings. By afternoon, you could be prototyping machine learning models for demand forecasting, followed by presenting your methodology and results to leadership. You'll end your day brainstorming with your tiger team on novel approaches to solving complex supply chain challenges.
- 2+ years of data scientist experience
- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- Experience applying theoretical models in an applied environment
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