Analytics & GenAI Architect-Life Sciences Technology

Job Family:

Data Science & Analysis (India)

Travel Required:

None

Clearance Required:

None

Position Summary:

The Analytics & GenAI Architect, Life Sciences Technology, designs and governs analytics and AI-enabled decision solutions that support commercial, market access, patient services, and medical affairs stakeholders. This role owns semantic architecture, KPI mart design, decision product development, and enterprise-grade GenAI orchestration patterns.

The architect ensures that analytics and AI solutions are scalable, explainable, production-ready, and aligned to defined business logic. This role operates at the intersection of data engineering, applied AI, and business translation — ensuring decision intelligence products are reliable, governed, and built for sustained enterprise use.

What You Will Do:

• Design and own semantic layer architecture and KPI marts supporting various life sciences commercial use cases (e.g., revenue performance, contracting analytics, engagement strategy, operational effectiveness, forecasting, etc.).
• Translate business requirements into structured analytics models, dimensional schemas, and reusable metric definitions.
• Architect and implement GenAI solutions using RAG and agentic patterns, including embeddings, vector search, tool integrations, and multi-step workflows.
• Establish AI governance standards including evaluation frameworks, versioning practices, guardrails, prompt management, and output validation controls.
• Define monitoring, telemetry, and performance benchmarks for analytics pipelines and GenAI workloads.
• Ensure analytics products are scalable, performant, and aligned to enterprise data contracts and governance standards.
• Partner with Cloud & Data Platform Architect to optimize curated data layers for advanced analytics and AI grounding.
• Collaborate with Full Stack Architect to expose analytics and AI through secure, performant APIs and user experiences.
• Contribute reusable accelerators including KPI templates, semantic models, evaluation harnesses, and RAG reference architectures.

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What You Will Need:

• 7–10 years of experience in analytics engineering, BI architecture, data science with at least 3 years of experience in Gen AI solution delivery.
• Strong expertise in Python with experience building scalable AI and analytical models.
• Experience designing multi-agent topologies (supervisor–worker, router, debate) with explicit planning, memory, and tool-use policies. Experience applying structured output and function-calling.
• Working knowledge of RAG architecture, vector search, embeddings, and reliable orchestration.
• Experience implementing advanced planning and reasoning patterns (React-style tool reasoning, task decomposition, dynamic routing), with guardrails for prompt injection, data leakage, and tool misuse.
• Expertise in Agentic AI frameworks leveraging LangChain and LangGraph. Knowledge of MCP servers and tool integration.
• Experience implementing production-ready practices including testing, monitoring, CI/CD integration, and observability.
• Experience collaborating across distributed engineering teams.
• Bachelor’s degree in data science, computer science, engineering, or related field.

What Would Be Nice To Have:

• Experience supporting life sciences analytics domains (e.g., contracting, performance analytics, incentive compensation, omnichannel strategy).
• Familiarity with AI governance practices in regulated environments.
• Experience implementing evaluation frameworks for GenAI quality and safety.

What We Offer:

Guidehouse offers a comprehensive, total rewards package that includes competitive compensation and a flexible benefits package that reflects our commitment to creating a diverse and supportive workplace.

About Guidehouse

Guidehouse is an Equal Opportunity Employer–Protected Veterans, Individuals with Disabilities or any other basis protected by law, ordinance, or regulation.

Guidehouse will consider for employment qualified applicants with criminal histories in a manner consistent with the requirements of applicable law or ordinance including the Fair Chance Ordinance of Los Angeles and San Francisco.

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