About This Role
This hybrid day shift role seeks an experienced data analyst with deep expertise in Python and modern generative AI frameworks, including a foundational understanding of large language models, model context protocols, and graph-based orchestration tools. The analyst will design and optimize data pipelines, embed responsible AI solutions, and partner with stakeholders to deliver reliable insights and automation that advance enterprise decision-making and societal outcomes through trustworthy data.
Responsibilities
- Drive end-to-end analytical solutions by collecting, cleaning, and structuring complex enterprise data sets using Python to enable accurate reporting, forecasting, and insight generation for business decision-makers
- Design and implement robust data pipelines and reusable code components in Python that streamline ingestion, transformation, and validation activities while improving maintainability, scalability, and operational efficiency
- Build and refine generative AI workflows that apply foundational knowledge of large language models to automate text analysis, summarization, and knowledge extraction in ways that respect governance and alignment with organizational standards
- Develop and orchestrate model context protocol-based integrations that reliably manage prompts, context windows, and tool connections, ensuring that AI-driven analytics remain consistent, auditable, and responsive to evolving business needs
- Construct and maintain LangGraph workflows that organize complex multi-step analytical and generative AI processes into clear graph structures, supporting experimentation, reuse, and transparent monitoring of model behavior
- Create Langchain-based chains and agents that securely connect language models with internal data sources and tools, enabling advanced querying, reasoning, and customized insight delivery for non-technical stakeholders
- Analyze large structured and unstructured data sets to identify trends, anomalies, and opportunities, then translate these findings into concise recommendations that influence strategy and measurable operational improvements
- Collaborate closely in a hybrid work environment with cross-functional partners such as product teams, engineering, and business operations to define analytical requirements and align outputs with strategic objectives
- Establish rigorous data quality checks, performance metrics, and documentation practices for all Python and generative AI solutions so that downstream users can trust reliability, reproducibility, and ethical use of insights
- Mentor junior analytics practitioners by sharing best practices in Python coding, prompt design, model evaluation, and data storytelling, thereby strengthening team capability and long-term institutional knowledge
- Optimize deployed generative AI and analytics workflows by monitoring performance, diagnosing issues, and implementing improvements that reduce latency, enhance accuracy, and increase value for internal and external stakeholders
