Algorithms, Analytical and Problem solving, Calculus, Data Analysis, Data Manipulation, Data science techniques, Data Visualization, Effective communication skills, Linear Algebra, Machine learning techniques, Mathematics, Neural Networks, NumPy, Optimization, Pandas, Probability, Python Programming, PyTorch, Scikit-learn, Statistics, TensorFlow
The role of a ML / AI data Scientist in a Generative AI team is critical for developing cutting-edge AI models and advancing the field.
About us
The Center for Competence (CoC) team on Generative AI within Siemens Healthineers Development center is responsible for providing productivity gains for the Product development lifecycle for the R&D organizations with a services, solutions and tool development to be used in the development of internal and external applications.
Job description:
As a Generative AI Data Scientist, your role revolves around leveraging generative models to create synthetic data or content for various applications. You will work at the intersection of data science, machine learning, and generative AI to develop innovative solutions. The job requires at least 6 years of industry experience.
Roles and Responsibilities:
Research and Innovation: Stay up-to-date with the latest advancements in machine learning, especially in the field of generative AI. Conduct research to identify potential applications of generative AI in solving real-world problems.
Model Development: Design and implement machine learning algorithms and models, including generative models like GANs (Generative Adversarial Networks) or VAEs (Variational Autoencoders). Optimize and fine-tune models for specific tasks and datasets.
Data Analysis: Collaborate with data scientists to analyze and preprocess datasets, identifying patterns and features relevant to the task. Perform exploratory data analysis to gain insights into the data.
Experimentation and Evaluation: Set up rigorous experiments to test the performance of AI models. Evaluate models using appropriate metrics and statistical methods. Iterate on models based on feedback and evaluation results.
Problem Solving: Work closely with domain experts and stakeholders to understand complex problems and formulate them as machine learning tasks. Develop creative solutions to challenging problems, often involving novel approaches.
Algorithm Development: Create and implement new machine learning algorithms or adapt existing ones for specific use cases. Optimize algorithms for efficiency and scalability.
Documentation and Communication: Document research findings, methodologies, and model architectures for internal use and potential publication. Communicate findings and progress effectively to both technical and non-technical team members.
Required Skill Set:
Siemens is a technology company focused on industry, infrastructure, mobility, and healthcare. Creating technologies for more resource-efficient factories and resilient supply chains to smarter buildings and grids, to cleaner, comfortable transportation and advanced healthcare, the company empowers customers to transform the industries that form the backbone of economies, transforming the everyday for billions of people.
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