Jobs at Lynx Analytics

Build and lead Lynx Analytics’ commercial function, own revenue targets, close strategic life sciences consulting engagements, define go-to-market strategy, establish sales systems, develop executive client relationships, and recruit the commercial team. The role focuses on selling AI, analytics, and professional services to pharmaceutical, biotech, and medtech organizations, with regular client travel.

Own revenue targets and manage the full sales cycle for AI, analytics, and professional services engagements in life sciences. Generate pipeline through prospecting and networking, qualify opportunities, develop proposals, negotiate, close deals, and expand client accounts. Maintain accurate forecasts and CRM hygiene while partnering with delivery and leadership on solution positioning, pricing, go-to-market execution, and account planning. Help build the commercial team and sales playbook as the company scales.

Lead end-to-end commercial projects for healthcare and pharmaceutical clients: define milestones, manage resources, budgets and timelines, liaise between clients and technical teams, ensure timely delivery, drive account growth, and pursue cross-sell/up-sell opportunities.

Own solution architecture for client engagements, including data models, system designs, integration patterns, and technology choices. Lead technical discovery, present architectural trade-offs, define reusable standards, and review delivery-team implementations. Build prototypes, proofs of concept, and early pipeline or application components to validate decisions. Partner with engineers to resolve ambiguity, establish testing and monitoring foundations, and contribute to internal tooling and knowledge-sharing.

Lead end-to-end data science engagements combining statistical modeling, machine learning, large-scale data transformation, and generative AI. Build agentic LLM applications, present analytical solutions to C-level stakeholders, lead workstreams, mentor junior data scientists, and collaborate with delivery and account teams. Develop reusable documentation and code libraries while contributing to internal research and education.

Build and operate scalable full-stack platforms supporting AI and analytics. Implement React/Next.js front-ends and Python back-ends, deploy on cloud (AWS/Azure/GCP), manage Docker and Kubernetes, maintain CI/CD, write tests, collaborate with data scientists, and follow data protection and compliance standards.

Build and maintain agentic AI pipelines, RAG and retrieval systems, integrate vector databases and knowledge graph components, write and test Python code, debug production AI systems, and collaborate with engineers and domain experts to deploy and document AI components.