IntroductionAt IBM, work is more than a job – it’s a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you’ve never thought possible. Are you ready to lead in this new era of technology and solve some of the world’s most challenging problems? If so, let’s talk.
IBM Consulting is IBM’s consulting and global professional services business, with market leading capabilities in business and technology transformation. With deep expertise in many industries, we offer strategy, experience, technology, and operations services to many of the most innovative and valuable companies in the world. Our people are focused on accelerating our clients’ businesses through the power of collaboration. We believe in the power of technology responsibly used to help people, partners and the planet.
IBM Cognitive Asset Engineering Services is looking for a motivated and seasoned Senior Data Scientist to our global team to deliver outstanding results for both internal/external clients and build a robust and repeatable product offering. You will be working in one of the top 10 assets in the group with highly collaborative teams in a dynamic and agile environment.
The IBM Consulting Modern Data Accelerators provides the foundation of data and analytics to enable business outcomes through the creation of cloud and hybrid cloud environments. It manages: Curation, Ingestion, Ingestion/Curation, Metadata, Operational Controls, Persist & Publish, and Data Security.
You will directly collaborate with CTO of the asset, architects, development leaders, system administrators, and testers who both enhance the product and establish and govern the architectural, development, and testing processes used across the organization.
Your Role and Responsibilities - Machine Learning Model Development: Design, develop, and implement machine learning models using Python libraries such as TensorFlow, PyTorch, or scikit-learn.
- Large Language Model Integration: Collaborate with architects to leverage Large Language Models (LLMs) like Watsonx AI, GPT-4, or Bedrock (if applicable) for project success.
- GenAI Project Experience: Bring at least 2 years of experience in GenAI projects, either for clients or IBM assets/products.
- GenAI Task Expertise: Demonstrate expertise in at least one GenAI task, such as:
- RAG content generation
- Code generation
- Code conversion
- AI Agent development
- Data mapping
- Graph RAG
- Solution Design and Requirements: Work closely with business SMEs to define project requirements, select appropriate AI/ML/GenAI models with architects, and set realistic model benchmarks to meet business needs.
- AI Solution Design with Ethics: Collaborate with architects to design AI solutions that incorporate knowledge of AI ethics.
Required Technical and Professional Expertise
- Machine Learning and GenAI: Strong understanding of machine learning and GenAI concepts. Awareness of ethical implications in GenAI, including bias, fairness, and privacy.
- Python and Data Science Libraries: Proficiency in Python and relevant data science libraries (e.g., NumPy, Pandas, scikit-learn). Practice on Graph database and Vector DB.
- Large Language Models: Familiarity with LLMs, including their architectures, capabilities, and limitations. Skill in crafting effective prompts to guide LLMs and obtain desired outputs. Ability to handle and prepare data for analysis, including data cleaning, normalization, and feature engineering
- GenAI Frameworks: Deep understanding of GenAI frameworks like TensorFlow, PyTorch, Hugging Face Transformers, Langchain, and Llama index.
- Latest GenAI Practices: Knowledge of the latest GenAI practices on at least two prominent LLMs (e.g., GPT-4, Llama3).
Preferred Technical and Professional Expertise
- Knowledge of Model Ops practices on cloud and containerization
- Familiarity with design lead development methodologies in complex data platforms and analytics
- Ability to work with data from the data platform and communicate insights effectively.
