Lead Data Scientist
New
F
FusemachinesAI Products and Services
USAFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- 6+ years
- Required Skills
- AWSPythonSQLMachine LearningPyTorchData scienceGenerative AI
Requirements
- Bachelor's, Master's, PhD, or advanced training in applied mathematics, engineering, computer science, or a similar related field.
- 6+ years of total experience in Data Science, Machine Learning & Generative AI.
- 4+ years of hands-on experience with AWS, including deep expertise in deploying models and managing compute environments.
- 2+ years of hands-on experience with IBM WatsonX.
- Experience with Agentic AI tools such as LangGraph or Google ADK.
- Experience with advanced RAG & multi-agent systems.
- Strong programming skills in Python, R, C++, and SQL.
- Hands-on experience with ML frameworks such as PyTorch or TensorFlow.
- Experience leading data science teams and managing multiple projects simultaneously.
- Strong problem-solving skills and attention to detail.
- Excellent written and verbal communication skills.
Responsibilities
- Lead a team of data scientists to develop innovative solutions to complex business problems.
- Mentor and develop the skills of junior data scientists and provide feedback and guidance to help them improve their work.
- Collaborate with cross-functional teams, including business stakeholders, product managers, software engineers, and data engineers to develop and implement data-driven solutions.
- Assess the business needs of clients and identify areas where AI can be used to improve processes, reduce costs, or increase revenue.
- Design and implement statistical models, machine learning algorithms, predictive analytics models, and agentic systems to solve business problems.
- Communicate technical insights and recommendations to non-technical stakeholders in a clear and concise manner.
- Stay up-to-date with the latest developments in data science, machine learning, and artificial intelligence, and apply new technologies and techniques to solve business problems.
- Manage end-to-end machine learning pipelines including building, deploying, and maintaining models and ensuring system stability.
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