Senior Data Scientist and Solution Architect
Bangalore, India
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- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
- Not stated
What you’ll work on
Full postingTranslate business objectives into scalable data science projects
Architect and deploy predictive models, LLM apps and agentic workflows
Partner with AI engineers to move models from research to production
Pilot methods such as RAG, fine-tuning and graph analytics
Mentor engineers and enforce reproducibility, bias and interpretability standards
From the employer’s posting
Translate high-level business objectives into scalable, rigorous data science projects.
Architect and deploy sophisticated models, including predictive analytics, LLM-powered applications, and agentic workflows.
Partner with AI Engineers to ensure seamless integration from research to production.
Pilot cutting-edge methodologies (e.g., Agentic Framework, RAG, fine-tuning, graph analytics)
Foster a culture of technical excellence and code reproducibility while ensuring all models adhere to enterprise ethics, bias mitigation, and interpretability standards.
What you’ll bring
All qualificationsCore experience
- Bachelor’s degree in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative field.
- 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
- Experience working with vector databases and semantic retrieval systems
- Experience in fine-tuning LLMs, advanced prompt engineering, and evaluating AI systems (eval frameworks, human-in-the-loop validation).
Preferred experience
- Master’s or PhD in Data Science, Statistics, Computer Science, or related quantitative field.
- Experience with MLOps practices (MLflow, Kubeflow, or similar) to manage the model lifecycle.
- Experience working with large-scale, unstructured datasets and multi-modal data.
Qualification wording
Bachelor’s degree in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative field.
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
Experience working with vector databases and semantic retrieval systems
Experience in fine-tuning LLMs, advanced prompt engineering, and evaluating AI systems (eval frameworks, human-in-the-loop validation).
Master’s or PhD in Data Science, Statistics, Computer Science, or related quantitative field.
Experience with MLOps practices (MLflow, Kubeflow, or similar) to manage the model lifecycle.
Experience working with large-scale, unstructured datasets and multi-modal data.
Education & alternatives
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
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.
Tools in this posting
- Python
- SQL
- MLflow
- Snowflake
- Spark
- pandas
- PyTorch
- scikit-learn
- TensorFlow
Source — Tool mentions in context
- 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.
- 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.
Posting record
Report an errorMay be filledThe employer has taken this listing down, so the role may be filled. Check their website before applying.
- Status in our records
- No longer listed
- First seen by us
- Jul 17, 2026 · 99 days ago
- Recorded sightings
- 102
- Last seen by us
- Oct 5, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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Job description
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
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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.
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Bangalore, India
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