Data Scientist Python Programmer

Overview

A Order Optimization Developer leverages Python and data science techniques to build and enhance systems that streamline and improve the efficiency of the order fulfillment process. This role bridges software development with supply chain logic to create automated solutions for complex ordering and logistics challenges.

Key Responsibilities

Develop and Implement Optimization Algorithms:

• Design, code, test, and deploy Python-based algorithms for optimizing various aspects of the order lifecycle, such as inventory allocation, demand forecasting, route planning, and warehouse slotting.
• Utilize mathematical modelling and optimization techniques (e.g., linear programming, heuristics, machine learning) to solve complex supply chain problems.
  
  

• Collect, clean, and preprocess large datasets from various sources (e.g., ERP systems, sales data) to feed into optimization models.
• Analyze order data to identify trends, patterns, and inefficiencies, providing actionable insights for system improvements.
  
  

• Build, maintain, and improve software applications and tools that support order optimization processes.
• Integrate optimization solutions with existing enterprise through APIs and other interfaces.
• Develop real-time dashboards and reporting mechanisms to visualize optimization outcomes and system performance.
  
  

• Collaborate closely with functional teams (e.g., supply chain analysts, operations managers, product managers) to understand business requirements and translate them into technical solutions.
• Debug and troubleshoot complex technical issues related to order optimization algorithms and systems.
• Participate in code reviews, ensuring code quality, scalability, and maintainability.
  
  

• Stay current with advancements in Python, data science, machine learning, and supply chain optimization technologies.
• Research and experiment with new tools and techniques to enhance the capabilities of order optimization solutions.
  
  

• Strong Python skills are essential, including experience with core Python, data manipulation libraries (Pandas, NumPy), and scientific computing libraries (SciPy), LSTM, Regression model etc.
• Familiarity with optimization libraries (e.g., PuLP, OR-Tools, SciPy.optimize) is highly desirable.
  
  

• Solid understanding of statistical analysis, data modelling, and machine learning concepts.
• Experience with ML libraries such as Scikit-learn, TensorFlow, or PyTorch.
• Ability to apply ML techniques for forecasting, clustering, and predictive analytics within the supply chain context.
  
  

• Knowledge of operations research principles and optimization methodologies (e.g., linear programming, integer programming, constraint programming, heuristics).
• Ability to translate business optimization problems into mathematical models.
  
  

• Experience with SQL and NoSQL databases.
• Familiarity with data warehousing concepts and ETL processes.
• Experience with cloud platforms (AWS, Azure, GCP) and their data services.
• Supply Chain Knowledge:
• Understanding of supply chain principles, Forecasting Algorithms, order fulfillment processes, inventory management and logistics.
  
  

• Experience with version control systems (e.g., Git).
• Understanding of agile development methodologies.
• Analytical and Problem-Solving Skills:
• Excellent analytical thinking and problem-solving abilities.
• Ability to break down complex problems into manageable components.
  
  

• Strong communication skills to effectively interact with technical and non-technical stakeholders.
• Ability to work effectively in a team environment.
  
  

Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, Operations Research, Industrial Engineering, or a related quantitative field.