AddressAs a Data Scientist at IBM, you will help transform our clients’ data into tangible business value by analyzing information, communicating outcomes and collaborating on product development. Work with Best in Class open source and visual tools, along with the most flexible and scalable deployment options. Whether it’s investigating patient trends or weather patterns, you will work to solve real world problems for the industries transforming how we live.
Your Role and Responsibilities
- Act as Data Scientist and SME for the GenAI use cases, having a good understanding of key LLMs, associated technologies and architectures (e.g. RAG).
- Lead the development and implementation of generative AI models and algorithms for various applications such as computer vision, natural language processing, and audio processing. Propose, define, and refine the GenAI implementation approach for a given use case.
- Collaborate with cross-functional teams to identify business opportunities and develop solutions that leverage generative AI technology.
- Work closely with the technical leadership team to develop and implement technical strategies that align with business objectives.
- Mentor and lead a team of Data Scientists and engineers, providing guidance and support to ensure the successful delivery of projects.
- Stay up-to-date with the latest trends and advancements in generative AI and related technologies, and apply this knowledge to develop innovative solutions.
- Ensure that all solutions are developed with a focus on scalability, reliability, and performance.
Required Technical and Professional Expertise
Must have skills:
- Technical skills with demonstrable experience in people management.
- Certification in data engineering, machine learning, or AI.
Must have demonstrable experience in some of the following:
- Strong programming skills in languages such as Python and R.
- Experience with generative AI frameworks and libraries such as TensorFlow, PyTorch, and GANbreeder.
- Strong understanding of machine learning algorithms, including deep learning techniques.
- Hands-on experience with cloud computing platforms such as AWS, Azure, or GCP.
- Experience with data visualization tools such as Tableau or Power BI.
- Experience with big data technologies such as Hadoop, Spark, and NoSQL databases.
- Experience with DevOps practices and tools such as Docker, Kubernetes, and CI/CD pipelines.
- Strong understanding of the ethical implications of AI and its applications.
