Must have

• Azure

• TensorFlow

• Keras

• scikit-learn

• SQL

• Python

• English (C1)

Requirements description

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
• Strong background in machine learning, statistical modeling, and predictive analytics.
• Proficiency in programming languages commonly used in machine learning, such as Python or R.
• Experience with machine learning libraries and frameworks, such as TensorFlow, Keras, or scikit-learn.
• Solid understanding of data engineering principles and experience with data manipulation, analysis, and visualization using SQL, Pandas, or similar tools.
• Familiarity with cloud computing platforms, such as AWS or Azure, and experience deploying machine learning models in a production environment.
• Strong problem-solving skills and the ability to translate business requirements into technical solutions.
• Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams and present technical concepts to non-technical stakeholders.
• Attention to detail and a strong commitment to delivering high-quality solutions.
• Prior experience in supply chain or related domains is a plus.

Offer description

Department Overview

The Supply Chain Analytics department is responsible for leveraging advanced analytics and machine learning techniques to optimize supply chain operations. By analyzing large volumes of data, we aim to improve forecasting accuracy, enhance inventory management, streamline transportation logistics, and optimize production planning. As a Machine Learning Engineer, you will work closely with data scientists and analysts to develop and deploy machine learning models that drive actionable insights and enable data-driven decision-making across the organization.

Your responsibilities

1. Collaborate with data scientists, engineers, and analysts to understand business requirements and design machine learning solutions that address supply chain challenges.
2. Develop, test, and deploy machine learning models that support demand forecasting, inventory optimization, transportation, and production planning.
3. Improve existing machine learning models by incorporating new data sources, enhancing feature engineering techniques, and optimizing model hyperparameters.
4. Design and implement data pipelines to ensure seamless data flow from various sources into the machine learning models.
5. Conduct exploratory data analysis and data cleansing to understand data quality issues and ensure accurate and reliable model performance.
6. Collaborate with cross-functional teams to integrate machine learning models into existing systems and processes, ensuring scalability and efficiency.

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