Junior Data Scientist
New
J
JobgetherMachine learning
Fully remote work opportunity within the United States.Full-TimeJunior
Salary85,000 - 102,000 USD per year
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Job Details
- Experience
- For candidates with a Master’s degree, 2+ years of professional machine learning experience; for PhD candidates, 1+ years of professional ML experience. At least 2 years of experience building, deploying, and maintaining machine learning models in production.
- Required Skills
- PythonSQLETLMachine LearningData engineering
Requirements
- Hold a Master’s or PhD in Mathematics, Statistics, Computer Science, or a related quantitative or technical discipline.
- Have 2+ years of professional machine learning experience with a Master’s degree, or 1+ years with a PhD.
- Have at least 2 years of experience building, deploying, and maintaining machine learning models in production.
- Bring strong analytical skills and solid knowledge of machine learning methodologies and algorithms.
- Have knowledge of data engineering and feature engineering.
- Have hands-on experience with data warehouses, feature engineering, ML pipeline automation, and model monitoring.
- Understand data warehousing and ETL processes.
- Be highly proficient in Python and SQL.
- Be able to collaborate with engineering teams on scalable ML pipelines and follow technical standards.
- Be able to work independently through ambiguous problems and take ownership with limited direction.
- Communicate technical concepts clearly to non-technical audiences.
- Experience with AWS SageMaker is a plus.
Responsibilities
- Identify opportunities to apply artificial intelligence and machine learning across products and contribute to implementation efforts.
- Design, test, and refine prompts for generative AI and large language model applications.
- Build, evaluate, deploy, and maintain machine learning models in production environments.
- Partner with product managers, software engineers, and subject-matter experts to translate business challenges into effective ML solutions.
- Contribute to scalable machine learning pipelines, data workflows, model deployment processes, and monitoring practices.
- Apply established best practices for ML development, automation, deployment, and ongoing model performance monitoring.
- Analyze data and model outputs to evaluate effectiveness and identify opportunities for improvement.
- Communicate technical findings, recommendations, and results to technical and non-technical stakeholders.
- Stay informed about machine learning research, industry practices, open-source projects, and emerging AI technologies.
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