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About the role
- Data Scientist designing and deploying AI-powered applications for querying and analyzing scientific data. Integrating large language models into workflows for enhanced data analysis and visualization.
Responsibilities
- Design and implement agentic AI systems that allow scientists to query Oracle databases and scientific data platforms using natural language, generating interactive plots and structured reports from preclinical data.
- Integrate large language models into scientific data workflows using both cloud-hosted services (Azure OpenAI) and locally deployed open-weight models (Ollama, vLLM, or similar), including prompt engineering, tool/function calling, guardrails, output validation, and structured output parsing.
- Design and implement retrieval-augmented generation (RAG) pipelines over scientific documents and database schemas to ground LLM responses in domain-specific context.
- Evaluate, benchmark, and select appropriate LLM backends (cloud vs. local, model size, quantization) based on latency, accuracy, cost, and data privacy requirements.
- Build scalable data models and ETL pipelines that surface scientific data through web-based applications and GUIs in Python (Plotly Dash, FastAPI).
- Use Docker to build, test, and deploy containerized applications across on-premises and Azure environments.
- Communicate effectively with scientific and technical stakeholders, including presenting methods, architectures, and results to broader audiences.
- Write detailed application and system documentation using GitHub Pages, Sphinx, or similar professional tooling.
Requirements
- Bachelor's degree (minimum) in Computer Science, Engineering, Mathematics, or a related quantitative field
- Advanced Python programming skills: clean, well-documented, production-quality code with appropriate testing and error handling
- Experience with SQL scripting and relational database systems (Oracle preferred), including query optimization and schema design
- Demonstrated ability to work with LLMs and AI agent frameworks — prompt engineering, retrieval-augmented generation (RAG), function/tool calling, structured output parsing, or similar orchestration patterns
- Hands-on experience deploying and serving LLMs locally using Ollama, vLLM, llama.cpp, or similar inference frameworks, including model selection, quantization trade-offs, and GPU resource management
- Proficiency with Python web frameworks for building interactive front-end applications (Plotly Dash and/or FastAPI), including working knowledge of HTML/CSS for UI refinement
- Experience with Docker for building and deploying containerized applications
- Strong Git workflows (branching, merging, pull requests) and familiarity with CI/CD tooling (GitHub Actions or similar)
- Comfortable working in Linux environments (Ubuntu), writing bash scripts, and managing applications on servers or VMs
- Excellent written and verbal communication skills with a demonstrated ability to document systems and workflows professionally.
Benefits
- Medical
- Dental
- Vision
- Short-& long-term disability
- Accidental death & dismemberment
- Life insurance programs
- Employee Assistance Program
- Travel insurance
- Retirement savings programs with company matching contributions
- Vacation
- Personal days
- Sick days
- End-of-year company shutdown
- Training and Development program
- Tuition Assistance program for advanced degrees
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