Job Description Summary

Job Description

Key Responsibilities

1. Thought Leadership in Applied Data Science & Analytics

• Define analytical strategies that address highvalue business problems and align with enterprise priorities.

• Lead endtoend data science solution design including data preparation feature development model selection and evaluation.

• Apply advanced statistical ML NLP and optimization methods to build robust highimpact models.

• Establish best practices around model rigor experimentation frameworks and interpretability.

• Deliver insights algorithms and intelligence that drive business decisionmaking and innovation.

2. LLM Architecture Training & Advanced Methodologies

• Serve as the subjectmatter expert for LLM development finetuning and applied research.

• Lead experimentation on novel training methods prompting approaches data curation strategies and evaluation techniques.

• Design and refine custom LLMs including work on instruction tuning retrievalaugmented generation (RAG) and reasoning frameworks.

• Develop agentic and tooluse capabilities including digital agents workflow orchestration and structured reasoning.

• Define reusable patterns and technical standards for LLMbased solutions across the organization.

• Evaluate GenAI technologies frameworks and model providers; guide buildversusbuy recommendations.

• Ensure LLM development aligns with Responsible AI security and compliance principles.

3. Model Delivery & Partnership with Engineering

• Collaborate with ML engineering teams to operationalize models and ensure reliable deployment.

• Provide technical guidance on model evaluation inference strategies and integration patterns.

• Offer expertise during design reviews architecture discussions and solution planning.

• Ensure final solutions are scalable maintainable and aligned with business needswithout owning infrastructure or cluster management.

4. Strategic Influence CrossFunctional Leadership & Communication

• Partner with product engineering and business leaders to define AI roadmaps and prioritize highimpact opportunities.

• Shape solution architectures and analytical approaches across multiple initiatives and teams.

• Translate complex technical concepts into clear recommendations for leadership and stakeholders.

• Guide teams through endtoend delivery of AI use cases from ideation to production adoption.

5. Mentorship Capability Building & Knowledge Sharing

• Mentor data scientists analysts and AI practitioners across teams.

• Lead internal learning initiatives workshops and communities of practice.

• Establish high technical standards and champion a culture of innovation rigor and humility.

• Stay ahead of advancements in LLMs multimodal AI reasoning models IR and AI safetyand help integrate them into our roadmap.

Qualifications

MustHave Qualifications

• Extensive experience solving complex business problems using ML NLP and advanced analytics.

• Deep handson expertise in LLM finetuning evaluation prompt engineering and GenAI development.

• Skilled with LLM toolkits and platforms (Hugging Face LangChain AWS Bedrock/SageMaker).

• Strong foundation in statistics ML theory NLP optimization and modeling best practices.

• Proven track record designing and delivering AI solutions endtoend in enterprise environments.

• Proficiency in Python; familiarity with distributed data tools (Spark Ray) is beneficial.

• Understanding of LLM architectures model optimization strategies and GPUbased training concepts (at a conceptual not infraadmin level).

• Experience with modern LLM techniques (PEFT prompt optimization RL ChainofThought).

Preferred Qualifications

• Masters or Ph.D. in Computer Science Machine Learning AI Data Science or related discipline.

• 6 years developing and deploying advanced analytics or AI solutions on major cloud platforms.

• 6 years deep learning experience with a focus on NLP and generative models.

• Experience collaborating with AI-native cloud platforms (OpenAI Anthropic AWS Azure Google Cloud).

• Background in agent-based systems reasoning models or reinforcement learning is a plus.

• Strong understanding of modern LLM advancements (long-context models multimodal systems alignment techniques agent ecosystems).

Inclusion and Diversity

GE HealthCare is an Equal Opportunity Employer where inclusion matters. Employment decisions are made without regard to race color religion national or ethnic origin sex sexual orientation gender identity or expression age disability protected veteran status or other characteristics protected by law.

We expect all employees to live and breathe our behaviors: act with humility and build trust; lead with transparency; deliver with focus and drive ownership always with unyielding integrity.

Our total rewards are designed to unlock your ambition by giving you the boost and flexibility you need to turn your ideas into world-changing realities. Our salary and benefits are everything youd expect from an organization with global strength and scale and youll be surrounded by career opportunities in a culture that fosters care collaboration and support.

Additional Information