Tiger Analytics
Principal Data Scientist - Optimization
Tiger Analytics, Houston, Texas, United States, 77246
Tiger Analytics is pioneering what AI and analytics can do to solve some of the toughest problems faced by organizations globally. We develop bespoke solutions powered by data and technology for several Fortune 100 companies. We have offices in multiple cities across the US, UK, India, and Singapore, and a substantial remote global workforce.
We are also market leaders in AI and analytics consulting in the CPG & retail industry with over 40% of our revenues coming from the sector. This is our fastest-growing sector, and we are beefing up our talent in the space.
We are looking for a Principal Data Scientist with a good blend of data analytics background, practical experience in optimizing replenishment strategies and allocating resources within supply chains, and strong coding capabilities to add to our team.
Key Responsibilities:
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 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.
Benefits:
This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.
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We are also market leaders in AI and analytics consulting in the CPG & retail industry with over 40% of our revenues coming from the sector. This is our fastest-growing sector, and we are beefing up our talent in the space.
We are looking for a Principal Data Scientist with a good blend of data analytics background, practical experience in optimizing replenishment strategies and allocating resources within supply chains, and strong coding capabilities to add to our team.
Key Responsibilities:
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 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.
Benefits:
This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.
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