Level

CiscoPosted 2w ago

Senior Data Scientist and Solution Architect

Senior Data Scientist and Solution Architect at Cisco scores 98 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

Bangalore, IndiaseniorFull time

AI in this role

pytorchtensorflowscikit-learnmlflow
prompt-engineeringragai-agentsfine-tuningml-opsai-evaluation

Meet the Team 

We are the Supply Chain Transformation AI Team within Cisco’s Supply Chain Operations. We are a diverse, fast-moving group of AI engineers and data scientists who collaborate directly with Product Operations. We don’t just analyze data; we transform it into actionable intelligence. By building advanced AI solutions, we empower our NPI (New Product Introduction) PMs, Product, and Test Engineering teams to anticipate market shifts, optimize workflows, and meet the evolving demands of our product lifecycle.

Your Impact 

You will lead the architectural direction and development of high-impact AI/ML solutions, transforming complex, ambiguous supply chain challenges into measurable business outcomes.

Core Responsibilities

  • Strategic Architecture: Translate high-level business objectives into scalable, rigorous data science projects.
  • Advanced AI/ML Development: Architect and deploy sophisticated models, including predictive analytics, LLM-powered applications, and agentic workflows.
  • Technical Leadership: Drive methodological rigor in experimental design, model evaluation, and statistical validation.
  • Cross-Functional Execution: Partner with AI Engineers to ensure seamless integration from research to production.
  • Innovation & Research: Pilot cutting-edge methodologies (e.g., Agentic Framework, RAG, fine-tuning, graph analytics) to maintain a competitive edge.
  • Mentorship & Governance: Foster a culture of technical excellence and code reproducibility while ensuring all models adhere to enterprise ethics, bias mitigation, and interpretability standards.

Minimum Qualifications

  • Bachelor’s degree in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative field.
  • Minimum of 7-10 years of professional experience in data science, analytics, or a related discipline, with demonstrated expertise in statistical analysis
  • Proven track record of applying correlation analysis and advanced statistical techniques in a business context.
  • Strong problem-solving skills, attention to detail, self-driven, and ability to manage multiple priorities in a fast-paced environment.
  • Generative AI & LLM Proficiency
    • Hands-on experience building and deploying LLM-powered applications in production
    • Experience with Agentic AI systems, autonomous workflows, tool calling, and multi-agent orchestration
    • Strong understanding of MCP (Model Context Protocol), A2A (Agent-to-Agent) communication patterns, and agent integration frameworks
    • Experience building RAG pipelines including embeddings, retrieval strategies, reranking, context management, and evaluation
    • Strong prompt engineering skills including prompt design, structured outputs, guardrails, and workflow optimization
    • Experience working with vector databases and semantic retrieval systems
    • Advanced Statistical & ML Expertise: Deep understanding of supervised/unsupervised learning, time-series analysis, and optimization techniques.
    • Experience in fine-tuning LLMs, advanced prompt engineering, and evaluating AI systems (eval frameworks, human-in-the-loop validation).
  • Programming & Data Stack: Expert-level proficiency in Python (Pandas, Scikit-learn, PyTorch/TensorFlow) and SQL. Familiarity with modern data engineering tools (Spark, Snowflake, or similar).
  • System Design: Ability to design end-to-end data pipelines that feed into production AI systems.
  • Communication: Exceptional ability to distill complex analytical findings into actionable business insights for non-technical stakeholders.

Preferred Qualifications

  • Master’s or PhD in Data Science, Statistics, Computer Science, or related quantitative field.
  • Proven track record of deploying models that have directly influenced supply chain or operational efficiency.
  • Experience with MLOps practices (MLflow, Kubeflow, or similar) to manage the model lifecycle.
  • Experience working with large-scale, unstructured datasets and multi-modal data.


Why Cisco? 

At Cisco, we’re revolutionizing how data and infrastructure connect and protect organizations in the AI era – and beyond. We’ve been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital worlds. These solutions provide customers with unparalleled security, visibility, and insights across the entire digital footprint.

Fueled by the depth and breadth of our technology, we experiment and create meaningful solutions. Add to that our worldwide network of doers and experts, and you’ll see that the opportunities to grow and build are limitless. We work as a team, collaborating with empathy to make really big things happen on a global scale. Because our solutions are everywhere, our impact is everywhere. 

We are Cisco, and our power starts with you. 


Disclaimer

To ensure that we hire the best talent in the right way, we follow a strict hiring process and recently, Cisco has been made aware of fraudulent recruiters claiming to be from the company. Please be advised that any communication from Cisco about careers will:

  • be in direct response to an application you have submitted through the company career site
  • begin with screening or an interview
  • originate from a Cisco email address, and
  • be conducted across email, phone, or WebEx

 

Cisco will never make a job offer without conducting an interview process or ask you for money in any way. If you have been requested to apply for a role or have received an offer from a site other than https://careers.cisco.com or cisco.wd5.myworkday.com, do not provide any personal identifying information, including your Aadhaar or other personal identifying number, birth certificate, banking information, driver's license, or passport.


If you are the target of a recruiting scam, consider filing a report with your local law enforcement authorities. Cisco bears no responsibility, and cannot be held liable, for any claims, damages, expenses, or other inconvenience resulting from or in any way connected to recruiting scams.


Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Prompt EngineeringRagAI AgentsFine TuningMl OpsAI EvaluationPyTorchTensorFlow

Questions you could be asked

  1. How do you structure and test a prompt to get consistent output from a language model?
  2. How would you design a retrieval step so the model answers from real data instead of guessing?
  3. How do you decide when an AI agent can act on its own versus asking for approval first?
  4. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  5. How do you monitor a model once it's live, and how do you know it needs retraining?

Adapt your resume

  • List these exact terms on your resume: Prompt Engineering, Rag, AI Agents, Fine Tuning, and Ml Ops. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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