Principal Data Scientist - Clinical / HLS, Google Cloud Gen AI, Agents, and Applied ML

Title: Principal Data Scientist Google Cloud Gen AI, Applied ML

Address: Remote

Duration: Contract to Hire

Requirements:
Focus: Clinical / HLS, Google Cloud Gen AI, Agents, and Applied ML

Job Description:

• Now is the time to bring your expertise to Insight. Healthcare and life sciences organizations are moving quickly to adopt generative AI, machine learning, and agentic systems, but many still face a critical challenge: converting complex clinical data and operational workflows into safe, measurable, production-ready AI solutions.
• We are seeking a Principal Data Scientist with deep experience in clinical or healthcare and life sciences environments, Google Cloud generative AI, agentic AI patterns, and applied machine learning. In this client-facing consulting role, you will help healthcare organizations design, validate, and operationalize AI solutions that improve decision support, streamline workflows, and unlock value from structured and unstructured clinical data.
• You will bridge the gap between clinical stakeholders, technical engineering teams, and executive leadership, ensuring that AI solutions are not only innovative, but also responsible, explainable, secure, and aligned to healthcare business outcomes.

Responsibilities:

• Clinical AI Solution Design: Lead the design of AI and ML solutions for healthcare and life sciences use cases, including clinical decision support, workflow automation, operational intelligence, patient-facing insights, and knowledge retrieval across complex healthcare data environments.
• Google Cloud Gen AI Architecture: Design and guide implementation of generative AI solutions using the Google Cloud AI ecosystem, including Vertex AI, Gemini, model evaluation workflows, retrieval-augmented generation patterns, and enterprise-grade deployment approaches.
• Agentic Systems for Healthcare Workflows: Architect and prototype agentic AI solutions that can reason across clinical, operational, and knowledge-based workflows while maintaining appropriate controls, traceability, and human-in-the-loop oversight.
• Applied Machine Learning: Develop and guide machine learning approaches for classification, prediction, summarization, entity extraction, document intelligence, and other healthcare-relevant use cases using structured, semi-structured, and unstructured data.
• Data Readiness and Clinical Context: Partner with client stakeholders to evaluate data quality, lineage, terminology, interoperability considerations, and clinical workflow fit before advancing AI use cases into production.
• Model Evaluation and Responsible AI: Define evaluation strategies for accuracy, relevance, bias, safety, drift, explainability, and clinical appropriateness, ensuring AI outputs can be trusted by healthcare stakeholders.
• Technical Advisory and Client Engagement: Serve as a senior technical advisor to client leaders, translating complex data science and Gen AI concepts into practical roadmaps, business value narratives, and implementation plans.
• Thought Leadership and Delivery Enablement: Mentor data scientists, engineers, and consultants while contributing reusable healthcare AI patterns, accelerators, evaluation frameworks, and delivery playbooks for Insight.

Requirements:

• Experience: 10+ years of experience in data science, machine learning, healthcare analytics, clinical AI, or applied AI solution delivery, ideally within consulting or enterprise client environments.
• Healthcare / HLS Domain Expertise: Strong understanding of clinical workflows, healthcare operations, clinical documentation, patient data, provider environments, payer/provider dynamics, or life sciences data use cases.
• Google Cloud AI Expertise: Hands-on experience with Google Cloud AI and data services, especially Vertex AI, Gemini, BigQuery, document AI, model deployment, and enterprise ML workflows.
• Generative AI and Agentic AI: Practical experience designing Gen AI and agentic solutions, including prompt engineering, tool use, orchestration patterns, RAG architectures, guardrails, and human review workflows.
• Machine Learning Depth: Strong foundation in supervised and unsupervised learning, NLP, model evaluation, feature engineering, experimentation, and production ML lifecycle practices.
• Responsible AI Mindset: Understanding of healthcare data sensitivity, Client protection, explainability, model risk, clinical validation, and governance expectations for AI-enabled healthcare solutions.
• Consulting Mindset: Exceptional communication skills with the ability to translate clinical and technical complexity into business-aligned recommendations for executives, clinical leaders, and technology teams.

Preferred Certifications

• Google Cloud / AI: Google Cloud Professional Machine Learning Engineer, Professional Data Engineer, or relevant Google Cloud AI certifications.
• Data Science / ML: Databricks Machine Learning, TensorFlow, or other relevant ML and analytics certifications.
• Healthcare / Governance: Certifications or training related to healthcare data, HIPAA, clinical analytics, Responsible AI, or AI governance are a plus.

Interview Process Note:

• The use of artificial intelligence tools (including but not limited to ChatGPT, Copilot, or similar AI assistants) during any phase of the interview process will result in immediate termination of the interview. This applies to all technical assessments, coding exercises, and take-home assignments.