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We are in search of a dedicated and inventive Generative AI Data Scientist to be part of our dynamic team.
In this strategic role, you will engage in critical use case analysis, architectural design, and the development of cutting-edge generative models aimed at addressing challenging business dilemmas. Your profound knowledge of machine learning, deep learning, and statistical modeling will greatly enhance EPAM's progress. Leverage your skills in this vibrant role to advance the frontier of AI technology.
Responsibilities
- Research and Development: Stay at the forefront of generative AI, deep learning, and associated areas through diligent research. Test various generative models, architectures, and algorithms to advance our technological capabilities
- Model Development: Architect, build, and roll out innovative generative models specific to desired applications. Engineer and refine deep neural networks, variational autoencoders (VAEs), generative adversarial networks (GANs), among other structures, to produce authentic and assorted synthetic data
- Data Preprocessing: Collaboratively work with data engineers and domain specialists to prepare and refine large-scale datasets. Implement statistical methods and data augmentation techniques to guarantee superior quality training data for generative models
- Model Training and Evaluation: Educate and fine-tune generative models using extensive datasets, utilizing approaches like transfer learning and unsupervised learning. Create metrics and benchmarks to judge model effectivity and produce insights that urge model enhancements
- Collaboration: Work alongside a varied team of data scientists, software engineers, insurance experts, and experience designers in integrating generative models into practical applications. Offer technical leadership and support to ensure efficient execution and deployment of generative AI solutions
- Innovation and Optimization: Investigate novel methods, frameworks, and tools to improve and optimize generative model performance. Stay informed of new trends and standard methodologies in the field and contribute to enhancing the organization's data science capacities
- Documentation and Reporting: Compile precise and clear technical documentation covering model structures, methodologies, and experimental results. Share insights and findings with both technical and non-technical audiences enhancing knowledge exchange and decision-making processes
Requirements
- Extensive experience with the development and application of generative models, including GANs, VAEs, and deep generative models
- Proficiency in programming languages like Python and in utilizing deep learning frameworks such as TensorFlow or PyTorch
- Solid grasp of concepts in machine learning, deep learning, and statistical modeling
- Background in handling large-scale datasets and applying preprocessing techniques
- Skill in data visualization and exploratory analysis tools
- Excellent problem-solving capabilities with a creative approach to devising innovative solutions
- Established track record of teamwork and engagement in multifaceted projects
- Robust research and self-learning skills, with an enthusiasm for keeping abreast of latest developments in generative AI and related fields
- Strong written and verbal communication abilities in English (B2 level), suited for explaining complex technical topics to diverse audiences
Nice to have
- Background in natural language processing (NLP) and text generation models
- Familiarity with cloud-based machine learning solutions and Generative AI services, such as Azure (Open AI, ChatGPT), Google Cloud, or AWS
- Knowledge of parallel computing and distributed training frameworks
- Contributions to the research community in generative AI or affiliated areas
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