Senior/Lead Data Scientist

As a Generative AI Data Scientist, you will play a critical role in use case evaluation, architecture, and development planning, developing and implementing state-of-the-art generative models to solve complex business problems. This is an exciting opportunity to apply cutting-edge techniques and push the boundaries of AI technology.

Qualifications:

-Proven experience in developing and implementing generative models, such as GANs, VAEs, or deep generative models.
- Strong proficiency in programming languages such as Python, with experience using deep learning frameworks such as TensorFlow or PyTorch.
- Solid understanding of machine learning, deep learning, and statistical modeling concepts.
- Experience working with large-scale datasets and preprocessing techniques.
- Proficiency in data visualization and exploratory analysis tools.
- Strong problem-solving skills and ability to think creatively to design innovative solutions.
- Excellent written and verbal communication skills, with the ability to effectively communicate complex technical concepts to both technical and non-technical audiences.
- Proven ability to work collaboratively in a team environment and contribute to cross-functional projects.
- Strong research and self-learning abilities, with a passion for staying up-to-date with the latest advancements in generative AI and related fields.


Preferred Skills:

- Experience with natural language processing (NLP) and text generation models.
- Familiarity with cloud-based machine learning platforms and Generative AI services, such as Azure (Open AI, ChatGPT), Google Cloud or AWS.
- Knowledge of parallel computing and distributed training frameworks.
- Publications or contributions to the research community in the field of generative AI or related disciplines.

Responsibilities:
- Research and Development: Conduct research and stay up-to-date with the latest advancements in generative AI, deep learning, and related fields. Explore and experiment with different generative models, architectures, and algorithms to enhance our capabilities.

- Model Development: Design, develop, and implement novel generative models tailored to specific use cases. Create and optimize deep neural networks, variational autoencoders (VAEs), generative adversarial networks (GANs), or other generative architectures to generate realistic and diverse synthetic data.

- Data Preprocessing: Work closely with data engineers and domain experts to preprocess and clean large-scale datasets. Apply statistical techniques and data augmentation methods to ensure high-quality input data for training generative models.

- Model Training and Evaluation: Train and fine-tune generative models using large-scale datasets, leveraging techniques such as transfer learning and unsupervised learning. Develop evaluation metrics and benchmarks to assess model performance and generate insights to guide model improvements.

- Collaboration: Collaborate with cross-functional teams, including data scientists, software engineers, insurance experts, and experience designers, to integrate generative models into real-world applications. Provide technical guidance and support to ensure successful implementation and deployment of generative AI solutions.

- Innovation and Optimization: Continuously explore new techniques, frameworks, and tools to optimize and enhance the performance of generative models. Stay informed about emerging trends and best practices in the field and contribute to the advancement of the organization's data science capabilities.

- Documentation and Reporting: Prepare clear and concise technical documentation, including model architectures, methodologies, and experiment results. Present findings and insights to both technical and non-technical stakeholders, contributing to knowledge-sharing and decision-making processes.