Data Scientist Associate Manager
New
USFull-TimeManager
Salary134,000 - 209,750 USD per year
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Job Details
- Experience
- 5+ years
- Required Skills
- DockerPythonSQLMachine LearningPyTorchTensorflowCI/CDR
Requirements
- 5+ years of hands-on experience in data science, analytics, machine learning, or related technical fields.
- Strong practical experience using Python for data analysis, modeling, and production-ready solutions.
- Experience with modern data science tools and technologies such as R, SQL, TensorFlow, and PyTorch.
- Strong understanding of statistical modeling, predictive analytics, and machine learning methodologies.
- Experience developing scalable solutions and applying data science techniques to business problems.
- Working knowledge of CI/CD pipelines, containerization technologies such as Docker, observability concepts, and cloud security fundamentals.
- Exposure to cloud AI platforms such as AWS Bedrock and/or Azure Foundry.
- Experience coaching, mentoring, or developing other data scientists.
- Strong communication skills with the ability to explain complex technical concepts to both technical and non-technical audiences.
- Strong project management, problem-solving, and strategic thinking abilities.
Responsibilities
- Lead the development and application of advanced analytics, machine learning models, and AI-driven solutions to support strategic business objectives.
- Review, evaluate, and communicate modeling approaches and results to technical teams, leadership, and business stakeholders.
- Develop communication strategies that explain the value and effectiveness of machine learning and predictive modeling initiatives.
- Partner with cross-functional teams to solve complex business challenges through data analysis, modeling, and implementation.
- Lead the design, development, and improvement of machine learning and predictive models that enhance decision-making.
- Mentor and support junior data scientists by providing technical guidance, coaching, and professional development.
- Identify opportunities to improve data sources, modeling techniques, analytical processes, and team capabilities.
- Manage analytical and modeling projects by defining scope, prioritizing tasks, and ensuring successful delivery.
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