Data Scientist with Python

Role Overview: You will be responsible for designing, developing, and implementing optimization models, focusing on linear programming and related techniques. Working with large-scale datasets, you will extract insights to drive business decisions. Using Python and Gurobi, you will build robust, efficient, and scalable solutions. Collaboration with cross-functional stakeholders to understand business needs and translate them into analytical solutions will be a key part of your role. You will independently manage end-to-end data science/optimization projects, validate, test, and deploy models in production environments, and communicate findings, insights, and recommendations clearly to both technical and non-technical audiences. Key Responsibilities: - Design, develop, and implement optimization models with a focus on linear programming and related techniques - Work with large-scale datasets to extract insights and drive business decisions - Build robust, efficient, and scalable solutions using Python and Gurobi - Collaborate with cross-functional stakeholders to understand business needs and translate them into analytical solutions - Independently manage end-to-end data science/optimization projects - Validate, test, and deploy models in production environments - Communicate findings, insights, and recommendations clearly to both technical and non-technical audiences Qualifications Required: - 6+ years of experience in Data Science, Optimization, or related fields - Strong programming expertise in Python - Hands-on experience with Gurobi or similar optimization solvers - Solid understanding of linear optimization techniques - Experience working with large datasets and data processing frameworks - Familiarity with Git for version control - Proficiency in Microsoft Excel - Ability to work independently and manage priorities effectively - Excellent communication and stakeholder management skills Additional Details: Preference will be given to candidates with experience in the Retail or Logistics domain. Role Overview: You will be responsible for designing, developing, and implementing optimization models, focusing on linear programming and related techniques. Working with large-scale datasets, you will extract insights to drive business decisions. Using Python and Gurobi, you will build robust, efficient, and scalable solutions. Collaboration with cross-functional stakeholders to understand business needs and translate them into analytical solutions will be a key part of your role. You will independently manage end-to-end data science/optimization projects, validate, test, and deploy models in production environments, and communicate findings, insights, and recommendations clearly to both technical and non-technical audiences. Key Responsibilities: - Design, develop, and implement optimization models with a focus on linear programming and related techniques - Work with large-scale datasets to extract insights and drive business decisions - Build robust, efficient, and scalable solutions using Python and Gurobi - Collaborate with cross-functional stakeholders to understand business needs and translate them into analytical solutions - Independently manage end-to-end data science/optimization projects - Validate, test, and deploy models in production environments - Communicate findings, insights, and recommendations clearly to both technical and non-technical audiences Qualifications Required: - 6+ years of experience in Data Science, Optimization, or related fields - Strong programming expertise in Python - Hands-on experience with Gurobi or similar optimization solvers - Solid understanding of linear optimization techniques - Experience working with large datasets and data processing frameworks - Familiarity with Git for version control - Proficiency in Microsoft Excel - Ability to work independently and manage priorities effectively - Excellent communication and stakeholder management skills Additional Details: Preference will be given to candidates with experience in the Retail or Logistics domain.