Data Analyst (Generative AI)
Cognizant — Chennai, Tamil Nadu
₹9–18 LPA (HireSetu estimate) · Full-time · hybrid
HireSetu listing reference d9d00c — confirm requirements below, then apply on the employer site.
Quick answer: A hybrid day-shift role in Chennai for someone with at least twelve years in data analytics who is also hands-on with LangGraph, LangChain and model context protocol integrations. That pairing of deep tenure and current GenAI tooling is the whole test of this listing.
From Cognizant's job posting
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.
Text from Cognizant's official job posting, formatted by HireSetu. The employer's own page is the final word on details.
The experience line is the filter
At least twelve years of progressive data analytics work in multinational settings. That single line will rule out most applicants, and it sits next to requirements for hands-on LangGraph work, LangChain chains and agents, and practical model context protocol knowledge. Few analysts with that much tenure have spent the last two years inside graph-based orchestration tools; those are new enough that almost nobody has years of them.
If you are at ten or eleven years with recent GenAI project work you can show in detail, the gap is small enough to argue. Below that, this posting is aimed at someone else.
The bulk of the week is pipelines and checking output
Strip out the GenAI language and much of the job is familiar analyst work: pulling messy enterprise data together in Python, writing reusable ingestion and validation components, keeping the code maintainable. The newer layer sits on top, wiring LangChain agents to internal data sources and organising multi-step processes as LangGraph workflows.
A large share of that layer is evaluation and monitoring. The listing names latency, accuracy and reproducibility as things you tune, and mentions keeping AI-driven analytics auditable. Judging whether an LLM's summary of a dataset is any good is slow, partly subjective work; it is central here, so a demo you built once will not carry you.
Chennai hybrid, with juniors in the mix
Day shift, hybrid, working alongside product, engineering and business operations. Mentoring is written into the responsibilities: sharing Python practice, prompt design and model evaluation with junior analysts. At this level that is a real slice of the week, not a footnote.
The other half is explaining results to business leaders in plain language, so your writing and presentation matter as much as your code. Certifications such as the Azure Data Scientist Associate are listed as preferred, which means they count as a bonus on top of the experience. Office days and team placement shift by project, so pin both down early.
Prepared from Cognizant's job posting and checked against it before publishing. See our Editorial Policy.
How to apply
- The Apply button opens this job on Cognizant's careers site (careers.cognizant.com).
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