Job Summary
This hybrid day shift role seeks an experienced data analyst with deep expertise in Python and modern generative AI frameworks including 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 d
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 multistep 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 nontechnical 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.
- Present complex analytical outcomes through clear narratives and visual summaries that help business leaders understand key drivers potential risks and societal implications of data informed decisions.
- Align day to day analytical priorities with company sustainability and social responsibility goals by highlighting how improved data use can reduce waste support inclusive services and promote responsible AI adoption.
Qualifications
- Demonstrate advanced proficiency in Python programming including experience with data manipulation libraries testing frameworks and performance optimization to support large scale analytics.
- Apply solid understanding of generative AI and large language model fundamentals such as tokenization context management evaluation methods and safety considerations in real world analytical solutions.
- Utilize practical knowledge of model context protocol to structure prompts maintain session state and orchestrate tool interactions that enable reliable and transparent AI powered data workflows.
- Employ hands on experience with LangGraph to design monitor and iterate on complex graph based workflows that coordinate multiple models tools and decision points in enterprise environments.
- Use Langchain effectively to integrate language models with data sources retrieval components and custom tools providing flexible and secure interfaces for question answering and analytics.
- Bring at least twelve years of progressive data analytics experience in multinational or similar settings including exposure to hybrid work models cross cultural collaboration and stakeholder engagement.
- Show strong communication and documentation skills that enable clear explanation of technical concepts risk tradeoffs and outcome implications to diverse audiences without relying on jargon.
Certifications Required
Preferred certifications include data analytics or AI credentials such as Microsoft Certified Azure Data Scientist Associate or similar industry recognized programs.
关于高知特 (Cognizant)
高知特(Cognizant)（纳斯达克代码：CTSH）作为一家AI Builder和相关技术服务提供商，致力于通过打造全栈AI解决方案，帮助企业将人工智能投资转化为实际价值。公司凭借深厚的行业经验、流程优化和工程技术专长，将企业独特的业务场景融入科技系统，赋能组织释放人才潜能，推动切实成果，并帮助全球企业在瞬息万变的环境中保持领先。如需了解更多详情，敬请访问 cognizant.ai 或关注@cognizant。
补充雇佣信息
薪酬信息截至本职位发布之日为准。Cognizant 保留在适用法律允许的范围内随时修改该信息的权利。
申请人可能需要通过现场面试或视频会议的方式参加面试。此外，候选人在每次面试时可能需要出示其当前所在州或政府签发的有效身份证件。
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