We are looking for passionate and driven professionals to join DataZymes, a next-generation analytics and data science company founded in 2016. At DataZymes, we focus on driving technology-led innovation and helping clients maximize the value of their data and analytics investments through cutting-edge platforms and consulting expertise. If you are excited about working on impactful solutions in the healthcare analytics space and want to be part of a high-performance, fast-growing team, we’d love to hear from you.

We are a data and analytics services
firm purpose-built for the pharmaceutical and life sciences industry. Our
clients span commercial analytics, medical
affairs, real-world evidence, and clinical operations. We are now building the capability
that will define the next phase of this business: production-grade agentic AI embedded in the workflows our clients
rely on daily.

This role is not a strategy position.
It is not a research position. It is a builder-leader role. You will architect and ship
multi-agent systems that operate autonomously across pharma data pipelines,
regulatory intelligence workflows, and cross-functional analytics use cases.
You will write code, own production deployments, and lead a small team doing
the same.

The ideal candidate has deep technical
fluency in agentic frameworks, understands the compliance,
data governance, and validation expectations of the pharma industry, and can translate both into working
systems, not slide decks.

Requirements

·     Design and build end-to-end agentic systems
combining LLMs, multi-agent orchestration, enterprise data pipelines, and
pharma-specific business logic. Ship production-grade systems, not prototypes.

·     Select and implement the right orchestration
approach across no-code, low-code, and pro-code patterns based on use case
complexity and client readiness.

·     Architect
retrieval and knowledge services (RAG, knowledge graphs) over structured and
unstructured pharma data: Rx, claims, engagement, clinical trial data, RWE
datasets, label text, and scientific literature. Includes RAG pipelines,
knowledge graphs for entity-relationship modeling (HCP, drug, indication, trial
networks), hybrid search, and retrieval evaluation frameworks.

·     Build observability, monitoring, and
evaluation frameworks to track agent behavior in production. Define guardrails,
failure modes, and human-in-the-loop escalation points.

·     Integrate with upstream pharma data platforms
(IQVIA, Symphony, Komodo, Veeva) and downstream delivery surfaces via APIs and
workflow hooks.

PHARMA DOMAIN APPLICATION

·     Translate
commercial analytics, medical affairs, and clinical operations workflows into
agentic automation opportunities. Target high-volume, high-complexity,
logic-intensive processes first.

·     Build agents
that operate over 21 CFR Part 11-aware environments. Understand what
auditability, validation, and traceability mean for autonomous systems in a
regulated context.

·     Develop
intelligent document processing pipelines for clinical study reports, drug
labels, HEOR submissions, and payer dossiers.

·     Apply
agentic AI to KOL identification and mapping, literature synthesis, competitive
intelligence, and signal detection workflows.

LEADERSHIP & CLIENT DELIVERY

·     Lead a team of AI engineers and ML
practitioners. Set technical direction, review architecture decisions, and
maintain a high bar for production quality.

·     Partner with client-facing teams to scope
agentic AI engagements: define the use case, design the solution architecture,
and own delivery accountability.

·     Communicate complex agent system behavior to
non-technical pharma stakeholders. Bridge the gap between what agents do and
what the business needs to trust.

·     Champion AI governance practices aligned with
industry standards: documented agent decision logic, bias audits, and
traceability to source data.

·     Build internal capability by mentoring team
members and establishing the firm's agentic AI playbook as a reusable asset.

TECHNICAL DEPTH (REQUIRED)

·     8+ years in software or ML
engineering; 3+ years with production LLM or agentic AI systems.

·     Hands-on proficiency with agentic
frameworks: LangGraph, LangChain, AutoGen, CrewAI, or equivalent. Model Context
Protocol (MCP) familiarity strongly preferred.

·     Direct SDK experience: Anthropic
(Agents SDK, tool use, Claude API), OpenAI (Assistants API, function calling),
Google (Vertex AI Agent Builder, Gemini API). Model Context Protocol (MCP)
strongly preferred.

·     Python fluency. Ability to build,
test, and deploy production code, not just notebooks.

·     Strong RAG architecture skills:
chunking strategies, embedding models, vector stores, knowledge graphs for
entity-relationship modeling (drug-indication-HCP-trial), hybrid search,
retrieval evaluation.

·     Cloud-native deployment: AWS,
Azure, or GCP. Containerization (Docker, Kubernetes), CI/CD,
infrastructure-as-code.

·     Observability tooling for AI
systems: logging agent traces, eval frameworks, cost management, drift
detection.

PHARMA / LIFE SCIENCES DOMAIN (REQUIRED)

·     Working knowledge of pharma commercial data
ecosystems: Rx/claims data, NPI-level analytics, market access, brand
performance

·     Familiarity with regulated data environments: GxP,
21 CFR Part 11, HIPAA-compliant data handling, audit trail requirements

·     Exposure to at least two of: medical affairs
analytics, real-world evidence, clinical operations data, or HEOR/market access
workflows

·     Comfort reading and reasoning over scientific and
regulatory documents: labels, clinical study reports, AMCP dossiers, payer
briefs

LEADERSHIP & COMMUNICATION (REQUIRED)

·     5+ years leading technical teams or delivery
workstreams, including mentoring engineers and managing project scope and
timelines

·     Track record of shipping production AI solutions
with measurable business impact, not just proof-of-concepts

·     Comfortable in executive-level conversations:
scoping engagements, presenting architecture trade-offs, and aligning on
governance expectations

·     Strong written communication. You can write a
crisp technical spec and a clear client-facing proposal without switching tools

GOOD TO HAVE

·     Experience with Veeva Vault, Medidata, or IQVIA
platform integrations

·     Knowledge of reinforcement learning from human
feedback (RLHF) and fine-tuning workflows

·     Familiarity with EU AI Act and emerging FDA
guidance on AI/ML in clinical and regulatory contexts

·     Prior consulting or services-firm experience:
multi-client delivery, proposal development, engagement management