Position Summary: 

The Data Scientist will develop next generation applications in medical diagnostics based on machine learning and bioinformatics.  This role will investigate new algorithms, predictive models, and information visualizations based on the data captured by Werfen’s market leading diagnostic instruments placed worldwide.

Key Accountabilities
Essential Functions:

• Research and development of novel machine learning algorithms for the existing products and next generation platform products
• Development of machine learning algorithms to expand product capabilities
• Participate in building new experiences, improve existing products, ensure quality assurance test designs and contribute to test systems architecture discussions
• Technical point of contact for AI and ML
• Understand Werfen’s products and services and introduce technology innovations with respect to functionality, performance, reliability, and scalability.

Budget Managed (if applicable)

• N/A

Internal Networking/Key Relationships

• To be determined based on department needs

Skills & Capabilities:

The ideal candidate for this position will exhibit the following skills and capabilities:

• Fluency with machine learning and scientific computing software, such as Scikit, TensorFlow and OpenCV
• Artificial intelligence software development experience using Python
• Database development, analysis
• Experience applying data science expertise to medical instrumentation, sensor or other hardware generated datasets is desired
• A passion for the cross-section of machine learning and biological data
• Strong presentation and general communication skills, ability to work in teams

Minimum Knowledge & Experience required for the position:

Education:

• Minimum of Master of Science, PhD preferred, in computer science or related field, with a focus on artificial intelligence preferred.

Experience:

• Minimum of 3 years work experience in R&D within the Healthcare sector is preferred

International Mobility:

• Required: No

Travel Requirements:

• Approximately 10% of time