Summary
Tiger Analytics is seeking a Principal Data Scientist with 10+ years of experience in supply chain optimization and inventory allocation to work on data science applications in the retail and CPG industry. The role involves refactoring an Optimization algorithm, utilizing advanced statistical techniques, developing predictive models, collaborating with cross-functional teams, and staying updated on industry trends.
Requirements
- Proven experience 10+ years working as a Data Scientist, with a focus on supply chain optimization and inventory allocation
- MS or PhD in Computer Science, Operations Research, Applied Mathematics, Machine Learning, or a related field
- Experience with using mathematical programming solvers such as Gurobi, Xpress MP, CPLEX, or Google OR Tools in applications
- Solid understanding of statistical methods, optimization techniques, and predictive modelling concepts
- Strong proficiency in programming languages such as Python, Pyspark and SQL, and experience working with data analysis and machine learning libraries
- Ability to apply various analytical models to business use cases
- Exceptional communication and collaboration skills to understand business partner needs and deliver solutions and explain to business stakeholders
Responsibilities
- Refactor the Optimization algorithm written in Python using Object Oriented Programming
- Work on the latest applications of data science to solve business problems in the Supply chain and optimization space of Retail and/or CPG
- Utilize advanced statistical techniques and data science algorithms to analyze large datasets and derive actionable insights related to replenishment optimization and inventory allocation
- Develop and implement predictive models and optimization algorithms to improve inventory management, reduce stockouts, and optimize resource allocation across the supply chain
- Collaborate with cross-functional teams to understand business requirements and translate them into data-driven solutions
- Design and execute experiments to evaluate the effectiveness of different replenishment strategies and allocation policies
- Monitor and analyze key performance indicators (KPIs) related to replenishment and supply chain allocation, and provide recommendations for continuous improvement
- Stay abreast of industry trends and best practices in data science, replenishment optimization, and supply chain management, and leverage this knowledge to drive innovation within the organization
- Collaborate, coach, and learn with a growing team of experienced Data Scientists
