Data Scientist
New
G
Great Gray GroupRetirement Services
Remote from the United StatesFull-TimeMiddle
Salary$90,000 - $120,000
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Job Details
- Experience
- 3+ years
- Required Skills
- AWSPythonSQLMachine LearningNumpyAzureFastAPIPandasscikit-learn
Requirements
- 3+ years of experience as a Data Scientist or ML Engineer, with a track record of delivering production-grade models.
- Proficiency in Python and the core data science stack: pandas, NumPy, scikit-learn, and FastAPI.
- Strong proficiency in SQL and experience querying large datasets in cloud environments (Azure, AWS).
- Experience building and deploying ML models and APIs, including familiarity with model serving and monitoring.
- Experience with large language models and AI platforms such as Azure AI Foundry or AWS Bedrock (preferred).
- Experience with AI-assisted development workflows using tools like Cursor and Claude (preferred).
- Hands-on experience with Azure and AWS services, including cloud-based ML workflows and managed AI services (preferred).
- Strong analytical mindset and ability to translate complex data findings into clear business recommendations.
- Strong collaboration skills and ability to thrive in an agile team environment.
Responsibilities
- Build, train, and deploy machine learning models and data pipelines using Python, pandas, NumPy, and scikit-learn, ensuring scalability and reliability in production.
- Design, implement, and continuously improve LLM-powered applications and Retrieval-Augmented Generation (RAG) pipelines, including prompt engineering, embedding strategies, vector store integration, and evaluation frameworks.
- Build and iterate on agentic AI systems that leverage tool use, multi-step reasoning, and orchestration frameworks to automate complex workflows and decision-making processes.
- Conduct exploratory data analysis (EDA) to uncover trends, patterns, and anomalies that inform business decisions and product strategy.
- Design and build data visualizations and dashboards that communicate key metrics and insights to both technical and non-technical stakeholders.
- Diagnose and resolve complex data and model issues, minimizing drift and continuously improving pipeline efficiency and model accuracy.
- Drive technical excellence through rigorous model evaluation, identifying opportunities for improvement, and enforcing best practices in code quality, experiment tracking, and reproducibility.
- Collaborate closely with cross-functional teams to translate business requirements into data-driven insights, models, and high-quality analytical solutions.
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