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Brief about the Team & Fractal:
Fractal Analytics is a strategic analytics partner to the most admired Fortune 500 companies globally and helps them power every human decision in the enterprise by bringing analytics & AI to the decision-making process.
Fortune 500 companies recognize analytics as a competitive advantage to understand customers and make better decisions. We deliver insight, innovation, and impact to them by leveraging Big Data, analytics and technology and help them drive smarter, faster, and more accurate decisions in every aspect of their business.
Fractal has consistently been rated as India’s best companies to work for, by The Great Place to Work® Institute. Fractal has been featured as a leader in the Customer Analytics Service Providers Wave™ 2019 by Forrester Research, and recognized as an “Honorable Vendor” in 2019 magic quadrant for data & analytics by Gartner.
Role Overview
The Sr. Data Scientist will lead the design-to-execution delivery of enterprise-grade GenAI and agentic decisioning product, translating approved architectures and workflows into scalable, governed AI solutions. This role bridges AI architecture, agent orchestration, and analytical execution, ensuring high-quality, production-ready implementations.
Key Responsibilities
1. Agentic AI Solution Delivery
Lead implementation of multi-agent GenAI workflows (planning, reasoning, tool-use, orchestration)
Translate business questions and workflows into agent-driven analytical solutions
Drive execution across use cases such as decision automation, copilots, and chatbots
2. LLM & GenAI Engineering
Design and implement LLM-powered pipelines (RAG, prompt orchestration, tool integration)
Ensure controlled, deterministic, and explainable agent behaviour
Optimize prompts, retrieval strategies, and grounding mechanisms for accuracy
3. Governed & Scalable Implementation
Enforce data, logic, and governance standards across agent workflows
Ensure alignment with approved architecture, semantic layers, and datasets
Build production-ready, scalable solutions
4. Validation, Observability & Quality
Define and execute evaluation frameworks (accuracy, consistency, hallucination control)
Establish observability for agent workflows (traceability, logs, reproducibility)
Triage issues across model behaviour, data quality, and workflow design
5. Collaboration & Solution Ownership
Partner with AI Architects on agentic system design and governance
Work with Data Engineering on data readiness and integration (RAG pipelines, feature layers)
Act as a solution partner to clients, driving discussions, alignment, and adoption
6. Delivery Leadership
Lead and mentor Data Scientists across GenAI and agentic implementations
Ensure predictable delivery, code quality, and review standards
Support pilots, production rollout, and handover
Mandatory Skills & Experience
Strong experience in GenAI, LLMs, and agentic architectures (multi-agent systems)
Hands-on expertise in RAG, prompt engineering, and LLM orchestration frameworks
Experience building enterprise-scale AI applications (chatbots, copilots, automation agents)
Solid grounding in data modelling, SQL, and analytical workflows
Experience with evaluation frameworks, observability, and responsible AI practices
Proven ability to act as a client-facing role
Success Measures
Robust, scalable agentic AI solutions delivered on time
High accuracy, explainability, and controlled LLM behaviour
Strong observability and evaluation coverage with minimal rework
Well-coordinated, high-performing delivery team
Work arrangement
No
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