Senior Healthcare AI Scientist Deploy Real-World Impact

McKinsey & Company; Boston (MA); On-site; USD 176,000 - 180,000; Full time

Job summary

McKinsey & Company is leading AI/ML initiatives at the intersection of healthcare and technology. You will design, build, and deploy ML/AI solutions across provider use cases, from clinical decision support to revenue cycle optimization, leveraging multi-modal data to drive real-world outcomes.

You will work with NLP and generative AI to surface insights that improve care delivery, and production systems will scale across major health systems with governance, data lineage, and HIPAA

Qualifications

• Bachelor's, Master's, or PhD in computer science, machine learning, applied statistics, mathematics, biomedical informatics, or a related quantitative field
• 5+ years of deep technical experience in distributed computing, machine learning and statistics related work
• 2+ years of professional experience applying machine learning and statistical modeling to complex, real-world datasets; healthcare or clinical data experience is highly valued
• Strong programming skills in Python (NumPy, pandas, scikit-learn, PyTorch/TensorFlow) and SQL; experience with PySpark or another big-data framework is a plus
• Experience with NLP or generative AI; including model development, fine-tuning, RAG pipeline design, or production deployment of LLM-based applications; is highly desirable
• Demonstrated ability to prototype statistical and ML algorithms and apply them to new problem domains with limited supervision
• Exceptional time management and the ability to manage multiple work streams in a fast-paced, largely autonomous environment
• Direct experience working with clinical or healthcare data; EHR (Epic, Cerner), claims, clinical notes, ADT feeds, or device/wearables data is preferred but not required
• Knowledge of healthcare data standards and interoperability frameworks (HL7, FHIR, ICD-10, SNOMED, OMOP CDM) is preferred but not required
• Experience deploying AI in regulated healthcare environments, including HIPAA-compliant data handling and model governance is preferred but not required
• Familiarity with healthcare operations domains: revenue cycle management (RCM), care management, clinical throughput, access optimization, population health is preferred but not required
• Strong communication skills; able to explain complex model behavior, trade-offs, and clinical implications to both technical teams and non-technical healthcare leaders

Responsibilities

• Design, build, and deploy ML/AI solutions across priority provider use cases; clinical decision support, care management, revenue cycle optimization, patient access, and agentic workforce tools
• Work with healthcare data (EHR/EMR, claims, clinical notes, imaging, device feeds) using NLP, generative AI, and predictive modeling to surface insights that improve care delivery
• Translate technical outputs into actionable recommendations for clinical and operational leaders and present findings to executive stakeholders at health systems
• Prototype and iterate on statistical and machine learning models with rigorous experimentation to validate clinical and business impact before scaling

Skills

Python

NumPy

Pandas

scikit-learn

PyTorch

TensorFlow

SQL

PySpark

NLP

Generative AI

Healthcare data

Model deployment

Communication

Education

Bachelor's/Master's/PhD in CS/ML/Statistics/Math/BI

Tools

PyTorch

TensorFlow

SQL

PySpark

Job description

McKinsey & Company is leading AI/ML initiatives at the intersection of healthcare and technology. You will design, build, and deploy ML/AI solutions across provider use cases, from clinical decision support to revenue cycle optimization, leveraging multi-modal data to drive real-world outcomes.

You will work with NLP and generative AI to surface insights that improve care delivery, and production systems will scale across major health systems with governance, data lineage, and HIPAA