Senior Data Scientist

Driving lasting impact and building long-term capabilities with our clients is not easy work. You are the kind of person who thrives in a high performance/high reward culture - doing hard things, picking yourself up when you stumble, and having the resilience to try another way forward. In return for your drive, determination, and curiosity, we'll provide the resources, mentorship, and opportunities you need to become a stronger leader faster than you ever thought possible. Your colleagues—at all levels—will invest deeply in your development, just as much as they invest in delivering exceptional results for clients. Every day, you'll receive apprenticeship, coaching, and exposure that will accelerate your growth in ways you won’t find anywhere else. When you join us, you will have: Continuous learning: Our learning and apprenticeship culture, backed by structured programs, is all about helping you grow while creating an environment where feedback is clear, actionable, and focused on your development. The real magic happens when you take the input from others to heart and embrace the fast-paced learning experience, owning your journey. A voice that matters: From day one, we value your ideas and contributions. You’ll make a tangible impact by offering innovative ideas and practical solutions, all while upholding our unwavering commitment to ethics and integrity. We not only encourage diverse perspectives, but they are critical in driving us toward the best possible outcomes. Global community: With colleagues across 65+ countries and over 100 different nationalities, our firm’s diversity fuels creativity and helps us come up with the best solutions for our clients. Plus, you’ll have the opportunity to learn from exceptional colleagues with diverse backgrounds and experiences. World-class benefits: On top of a competitive salary (based on your location, experience, and skills), we provide a comprehensive benefits package to enable holistic well-being for you and your family. You will 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; with clear outcome accountability. Healthcare is one of the most consequential domains in which AI can create real, durable impact; and it is also one of the most complex. Provider organizations are sitting on enormous clinical and operational data assets, but turning those assets into deployed, trusted AI systems at scale requires the kind of rigorous, multi-disciplinary approach that McKinsey and QuantumBlack Labs are uniquely positioned to deliver. You will work on AI systems that directly influence how care is delivered, how clinicians spend their time, how patients flow through the system, and how resources are allocated. The models you build are not proofs-of-concept; they are production systems used by some of the largest health systems in the country. That combination of technical rigor, healthcare domain depth, and real-world deployment scale is rare, and it is what makes your work exceptional. You will be solving the hardest data and AI challenges in healthcare with direct client exposure and measurable clinical and operational results. You will work with rich, multi-modal clinical datasets (EHR/EMR, claims, clinical notes, imaging, device feeds) using advanced NLP, generative AI, and predictive modeling to surface insights that improve care delivery. You will leverage and extend QuantumBlack Labs' latest generative AI and agentic frameworks to build autonomous clinical workflows; from AI-assisted documentation to intelligent care-management agents that act on behalf of clinicians. You will write optimized, production-ready code; develop reusable ML pipelines and model assets that can scale across multiple health systems in the portfolio. You will partner with QuantumBlack Labs data scientists, ML engineers, and product managers, as well as Social, Healthcare, and Private Entities (SHaPE) clinicians and transformation consultants, to translate complex healthcare requirements into production AI systems. You will contribute to McKinsey's growing body of healthcare AI knowledge; writing papers, presenting at industry conferences, and shaping internal capability-building programs. You will design AI applications to operate safely and reliably in regulated healthcare environments, partnering with data engineers to ensure robust data pipelines, governance, lineage, and HIPAA compliance. You will translate technical outputs into actionable recommendations for clinical and operational leaders, and present findings and implications to executive stakeholders at health systems. You will rapidly prototype and iterate on statistical and machine learning models, using rigorous experimentation to validate clinical and business impact before scaling.

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