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.
Data Science And Machine Learning
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.
Optimization Techniques
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.
Data Engineering And Databases
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.
Software Development Practices
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.
Communication And Collaboration
Strong communication skills to effectively interact with technical and non-technical stakeholders.
Ability to work effectively in a team environment.
Educational Background
Bachelors or Masters degree in Computer Science, Software Engineering, Data Science, Operations Research, Industrial Engineering, or a related quantitative field.
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