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.
コグニザントについて
コグニザント（NASDAQ: CTSH）は、AI Builderおよびテクノロジーサービスプロバイダーとして、お客様にフルスタックのAIソリューションを構築することで、AI投資と企業価値を結ぶ架け橋となっています。業界、ビジネスプロセス、エンジニアリングに関する当社の深い専門知識を活かし、組織固有のビジネス環境をテクノロジー・システムに組み込みます。これにより、人間の可能性を最大限に引き出し、確かな成果を実現するとともに、急速に変化する世界においてグローバル企業が常に一歩先を行くための支援を行っています。 詳細については、cognizant.ai をご覧ください。
雇用に関する追加情報
本募集に記載されている報酬情報は、掲載日時点で正確なものです。Cognizantは、適用される法令に従い、いつでも本情報を変更する権利を留保します。
応募者は、対面またはビデオ会議による面接への参加を求められる場合があります。また、各面接の際に、現在有効な州政府または政府発行の身分証明書の提示を求められる場合があります。
Cognizantは機会均等雇用主です。応募および選考において、人種、肌の色、性別、宗教、信条、性的指向、性自認、国籍、障がい、遺伝情報、妊娠、退役軍人の地位、その他連邦法・州法・地方自治体の法律により保護されるいかなる特性に基づく差別も行いません。
