Responsibilities related to data science and machine learning in a manufacturing context. Key tasks include collaborating with cross-functional teams to create impactful data solutions, analyzing large datasets to enhance business metrics, designing and implementing machine learning models, developing scalable computer vision solutions, and maintaining web applications for data initiatives. Additionally, it involves interpreting data insights, communicating findings to stakeholders, and delivering presentations with visualized data and business conclusions. ESSENTIAL DUTIES AND RESPONSIBILITIES: Collaborate with cross-functional teams to develop high-impact data science solutions that improve productivity and operations metrics Analyze large-scale data and develop machine learning/AI models to drive business value and improve KPIs Design, prototype, and implement machine learning models and algorithms to solve specific Manufacturing problems Researching and developing scalable computer vision and machine learning solutions for complex problems Create and maintain web applications to support data science initiatives and facilitate data-driven decision making Interpret actionable insights from data and metadata sources, communicating findings to stakeholders for product improvement Prepare and deliver presentations with data visualizations and business conclusions
REQUIRED: Bachelor’s degree in Data Science, Computer Science, Computer Engineering, Software Engineering, and Robotics & AI Engineering with relevant industry or academic experience in data analytics 2 - 5 years of experience in Data Science field PREFERRED: Knowledge of AI/ML model development and lifecycle management. Experience with cloud-based solutions and collaborative tools (e.g., Github, Docker). Experience in Computer Vision and image processing techniques. Experience in web development. Familiarity with advanced machine learning techniques (e.g., neural networks, NLP, deep learning). Ability to communicate complex technical concepts to non-technical stakeholders. Proactive approach to learning and implementing emerging technologies. SKILLS: Proficiency in SQL, Big Data platforms, and cloud services (e.g., AWS) Strong background in applied statistics, statistical modeling, and practical experience with ML and AI algorithms. Required: Python, Computer Vision, ML model development Familiar with collaborative solutions, model & code versioning (Github), and solution packaging (Docker) Strong communication, analytical, and problem-solving skills.
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