Job Purpose and Impact

The Associate Optimization Scientist will improve learn and contribute how to optimize your scheduling process in supply chain planning space. Optimize daily production schedules by mining, analyze basic data using optimization methods and AI tools for data driven decision making.  In this role, you will deploy predictive and optimization models at scale for digital supply chain products that help deliver significant value to our customers, businesses and functions.

Key Accountabilities

• Stay ahead of new analytic methodologies and technologies and collaborates with business functions to drive work solutions and direction.
• Develop and apply algorithms or models to key business metrics with the goal to improve operations or answer business questions.
• Take ownership for the entire data science workflow including well defined project scoping, exploratory data analysis, model development, deployment, and monitoring.
• Benchmark for jobs related to information technology system design, development and maintenance.
• Perform research, analysis and modeling on organizational data.
• Develop algorithms, dashboards, information tools and query to clean, model, integrate and evaluate data sets (when needed)
• Contribute to design, and implementation of new features, and deploy optimization solutions for PSO (Production Scheduling and Optimization) product
• Handle basic issues and problems under direct supervision, while escalating more complex issues to appropriate staff.
• Other duties as assigned

Qualifications

MINIMUM QUALIFICATIONS

• Bachelor’s / Master degree in a related field of Data Science and Optimization.
• Computer Science Engineering, Operations Research
• MBA in Operations, Business Analytics from reputed institutes
• Other minimum qualifications may apply

PREFERRED QUALIFICATIONS

• Basic knowledge of common statistical knowledge, Optimization skills, usage of programming languages like Python etc.
• Beginning-to-working knowledge of data science concepts, methods and tools
• One or more year of work experience in a research field

Disclaimer

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