Principal Data Scientist - Optimization

August 3

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Tiger Analytics

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AI & Analytics for today’s business challenges.

Machine Learning • Predictive Analytics • Forecasting • Optimization • Natural Language Processing

1001 - 5000

Description

• Responsible for refactoring 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.

Requirements

• Proven experience 10+ years working as a Data Scientist, with a focus on supply chain optimization and inventory allocation. • MS or PhD in OR, IE, Math and ACO (Algorithm, Combinatorics, and Optimization) • 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.