Data Scientist

Data Scientist

Location: Eagan, MN (Onsite)

Responsibilities

• Lead the development and implementation of machine learning models to solve complex business problems and enhance decision-making processes.
• Oversee the application of statistical techniques and exploratory data analysis to extract meaningful insights from large datasets.
• Provide expertise in Azure Open AI Service and Cloud AutoML to optimize and automate data-driven processes.
• Collaborate with cross-functional teams to integrate AI solutions into existing systems and workflows.
• Utilize Airflow for orchestrating complex data workflows and ensuring seamless data pipeline operations.
• Develop and maintain knowledge graphs to improve data accessibility and enhance search engine optimization.
• Implement MLOps practices to streamline the deployment and monitoring of machine learning models in production.
• Apply deep learning techniques using PyTorch and TensorFlow to advance the company’s AI capabilities.
• Conduct natural language processing and dialogflow projects to enhance customer interactions and service delivery.
• Use OpenCV for computer vision tasks that contribute to innovative product development.
• Engage in continuous learning and application of the latest advancements in artificial intelligence and machine learning.
• Ensure the ethical use of AI technologies and contribute to the company’s commitment to responsible AI practices.
• Drive the adoption of AI and machine learning across the organization to foster a culture of innovation.
• Collaborate with stakeholders to identify opportunities for AI-driven improvements and efficiencies.

Qualifications

• Possess a strong foundation in Statistics and experience with R Statistical Package and Python.
• Demonstrate proficiency in Azure Open AI Service and Cloud AutoML for scalable AI solutions.
• Exhibit expertise in machine learning frameworks such as PyTorch and TensorFlow.
• Have experience with natural language processing and dialogflow for enhanced communication systems.
• Show capability in using OpenCV for computer vision applications.
• Understand the principles of MLOps for effective model deployment and management.
• Be knowledgeable in search engine optimization techniques to improve data discoverability.