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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Scientist - **Company:** McKinsey & Company - **Location:** Dallas, TX, United States - **Experience:** Expert - **Salary:** $175,500.0 - $180,000.0 - **Contract:** Permanent contract - **Skills:** Cerner, Artificial Intelligence, Big Data, Health Informatics, Clinical Data Repository, Computer Programming, Distributed Systems, Python (Programming Language), Machine Learning, NumPy, Operational Data Store, Tensorflow, Software Deployment, SQL Databases, Data Processing, Pytorch, Fast Healthcare Interoperability Resources, Large Language Models, Electronic Medical Records, Pandas, Pyspark, Scikit Learn, Information Technology, Health Level Seven International, Machine Learning Operations, Data Pipelines - **Published:** August 21, 2026 - **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/18002392?backUrl=%2Fcareer%2F18002392%2FSenior-Data-Scientist-Texas-Dallas ## About the Role * 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 ## Description 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. ## Related Videos - [Vectorize all the things! 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