About the Company

Job Description

This role offers an exciting opportunity to contribute to the development and implementation of Digital Factory solutions and Machine Learning models across various critical functions, including Manufacturing, Quality Control, and Supply Chain Management. The successful candidate will be responsible for supporting various aspects of data management and analysis, playing a crucial role in driving data-informed decision-making.

Responsibilities

• Participate in the development and deployment of Digital Factory solutions as well as Machine Learning models, applicable to Manufacturing, Quality, and Supply Chain operations.
• Assist with data collection, cleansing, preparation, and transformation activities from a variety of data sources, ensuring data integrity and consistency for analytical purposes.
• Contribute to the design and development of insightful dashboards and reports utilizing data visualization tools such as Power BI or Tableau.
• Execute fundamental analytics and visualization tasks to extract actionable insights and pinpoint areas for process improvement.
• Provide support for the maintenance of existing Machine Learning models, which includes ongoing data monitoring and model retraining procedures.
• Engage in small-scale Proof of Concept (PoC) initiatives and pilot programs under the guidance of senior team members.
• Document use cases comprehensively, develop well-structured code with appropriate mentorship, and actively participate in knowledge-sharing sessions.
• Facilitate the integration of models into production environments and conduct preliminary testing procedures.

Qualifications

• A Bachelor’s or Master’s degree in a quantitative field such as Data Science, Computer Science, Statistics, or Engineering, supplemented by 0-3 years of relevant professional experience.
• Demonstrated proficiency in Python and/or R programming languages for data analysis, including practical experience with libraries such as Pandas, NumPy, Matplotlib, and Seaborn.
• A foundational understanding of statistical concepts, encompassing distributions, correlation analysis, regression modeling, and hypothesis testing.
• Familiarity with SQL or other database querying tools, for instance, pyodbc, sqlite3, and PostgreSQL.
• Exposure to Machine Learning algorithms, including linear/logistic regression, decision trees, k-Nearest Neighbors (k-NN), and Support Vector Machines (SVM).
• Basic knowledge of Jupyter Notebooks for data exploration and experimentation, as well as version control systems utilizing Git/GitHub.
• Effective communication skills in English, both written and verbal, with the ability to articulate complex technical concepts in a clear and concise manner.
• A collaborative spirit, a proactive approach to learning, and the ability to adapt effectively within a dynamic and multicultural work environment.
• Exposure to, or a demonstrated interest in, manufacturing technologies, such as stamping, molding, and assembly processes, is highly desirable.
• Exposure to cloud platforms like AWS or Azure, or related services such as S3, SageMaker, and Redshift, is considered a distinct advantage.
• Hands-on experience in image data preprocessing techniques (e.g., resizing, Gaussian blur, Principal Component Analysis (PCA)) or computer vision projects is beneficial.
• Interest in AutoML tools and transfer learning methodologies is a plus (e.g., Orange).

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