Data Science Sr. / Lead / Architect –
Healthcare & Life Sciences
Role Overview
· We are looking for an
experienced Data Science Sr. / Lead / Architect to architect and deliver
enterprise AI solutions across Commercial, R&D, and Sales & Marketing
within Healthcare & Life Sciences.
· The role combines AI
architecture, GenAI/Agentic AI engineering, technical leadership, client
consulting, solutioning, and rapid prototyping.
Key Responsibilities
AI Architecture & Technical
Leadership
· Architect scalable, secure,
production-ready AI/ML solutions spanning data, models, LLMs, agents,
applications, APIs, infrastructure, security, and observability.
· Lead and mentor Data Scientists
and Senior Data Scientists, driving technical excellence, coding standards, and
delivery practices.
Consulting & Solutioning
· Lead executive discovery with
Sales, Account Management, Practice Leaders, and Delivery teams to understand
client priorities, challenges, and transformation roadmaps.
· Drive proposals, presentations,
solution architectures, demos, business cases, GTM assets, and thought
leadership.
Generative AI & Agentic AI
· Design and implement RAG
architectures across structured and unstructured data, including embeddings,
vector search, retrieval, reranking, and context management.
· Design multi-agent systems
using LangGraph, including memory, planning, reasoning, tool use, and
autonomous task execution.
· Integrate LLM applications with
enterprise data, APIs, and business systems.
ML Engineering & MLOps
· Design and operationalize
end-to-end ML/AI pipelines with Data Engineering and MLOps teams, covering
deployment, monitoring, versioning, evaluation, and lifecycle management.
· Rapidly build POCs, prototypes,
and demos and translate them into measurable business value and ROI.
Required Qualifications & Experience
Experience
· 5–7+ years designing, building,
and deploying AI/ML solutions.
· Experience in Pharmaceutical,
Healthcare, or Life Sciences IT, including healthcare datasets such as EHR/EMR,
claims, clinical, or imaging.
· Experience delivering
production AI/ML solutions within quality, compliance, and regulated
environments.
· Experience developing
predictive models and/or digital twins.
Technical Skills
· Strong Python and SQL skills
with understanding of data modelling, ETL/ELT, pipelines, and data warehousing.
· Strong knowledge of supervised,
unsupervised, and deep learning using TensorFlow and/or PyTorch.
· Hands-on experience with LLMs,
Generative AI, RAG, LangChain/LangGraph, prompt engineering, tool calling,
structured outputs, guardrails, evaluation, and model selection.
· Experience integrating AI
applications with enterprise data, APIs, and business systems.
· Understanding of AWS/cloud
architecture, MLOps tools such as MLflow, Kubeflow, or SageMaker, and
containerization, CI/CD, deployment, monitoring, and production operations.
· Strong communication,
stakeholder management, and technical leadership skills.