Lead Data Scientist - IBM Watson
New
Fully remote role within the United States.Full-TimeLead
Salary not disclosed
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Job Details
- Experience
- 6+ years of experience in Data Science, Machine Learning, or Generative AI roles.
- Required Skills
- AWSPythonSQLMachine LearningData scienceTensorflowGenerative AI
Requirements
- Bachelor’s, Master’s, PhD, or equivalent advanced training in Computer Science, Engineering, Mathematics, or related field.
- 6+ years of experience in Data Science, Machine Learning, or Generative AI roles.
- 4+ years of hands-on experience with AWS cloud environments and production model deployment.
- 2+ years of experience working with IBM WatsonX or IBM Watson-based solutions.
- Strong experience with agentic AI frameworks such as LangGraph, Google ADK, or similar tools.
- Deep understanding of RAG architectures and multi-agent AI systems.
- Strong programming skills in Python, SQL, and familiarity with R or C++.
- Hands-on experience with ML frameworks such as TensorFlow or PyTorch.
- Proven leadership experience managing data science teams and multiple concurrent projects.
- Strong communication, problem-solving, and stakeholder management skills.
Responsibilities
- Lead and mentor a team of data scientists, guiding technical direction and supporting professional development.
- Collaborate with cross-functional stakeholders to define business problems and translate them into AI-driven solutions.
- Design and implement machine learning models, statistical systems, predictive analytics, and agentic AI architectures.
- Develop and manage end-to-end ML pipelines, including training, deployment, monitoring, and maintenance.
- Apply advanced AI techniques such as RAG systems, multi-agent frameworks, and generative AI solutions.
- Analyze business requirements to identify opportunities for cost reduction, revenue growth, and process optimization.
- Communicate technical findings and recommendations clearly to both technical and non-technical audiences.
- Ensure data quality, model reliability, and production stability across deployed AI systems.
- Stay current with emerging trends in AI, machine learning, and data science, integrating new methods where relevant.
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