Manager, Data Scientist

About the opportunity

Description Healthcare Analytics Solutions (HAS) is an innovative team within Quest Diagnostics that leverages Quest data to develop products and services to improve outcomes in healthcare across many different markets (Pharma, Clinical Trials, Health Plans/Payers, Hospitals/Health Systems, and Public Health agencies). Join HAS to build, productionize, and operationalize clinical ML products using billions of results from Quest laboratory data. You will partner with clinicians, engineers, and product teams to deliver robust, compliant, and well‑documented models that impact patient care and downstream products. In this role, you will be responsible for understanding and implementing the latest advances in the field of machine learning applied to healthcare use cases. Fully remote, minimal travel required; strong emphasis on hands‑on production experience and pragmatic problem solving. Responsibilities • Machine learning model garden used to create predictive analytics-based data products in healthcare. • Monitoring and surveillance of state of art research in machine learning in relation to healthcare and clinical AI. Incorporating innovation as appropriate into our ML garden and solutions. • Thought leaders support for ML Ops, including containerization, model serving, performance tuning, rollout strategies, and observability (drift, performance, alerts). • Model governance: reproducibility, versioning, bias/fairness checks, and audit-ready documentation. • Integration of advanced analytics and machine learning models into business products and services including business intelligence dashboards and real-time analytics. • Curation of data sets from Quest and non-Quest data sources in support of deriving business insights driven by advanced analytics. • Cross-functional partnership with clinicians and product owners to define outcomes, acceptance criteria, and validation plans. • Mentor and raise engineering standards across the team: coding best practices, testing, and deployment patterns. • Translate technical results into clear explanations and recommendations for technical and executive stakeholders. Qualifications Required: • 2+ years evidence gaining deep knowledge about advanced machine learning concepts, new model architectures as well as research-level evaluation of promising model designs and architectures. • 5+ years relevant experience with Python and SQL; production-grade code and testing practices. • Practical experience with model serving and monitoring, CI/CD for ML, and feature pipeline orchestration. • Experience working with healthcare data (labs, EHR, claims) and familiarity with PHI handling/HIPAA considerations. • Excellent statistics, model evaluation, and pragmatic approach to validation. • Excellent communication and problem‑solving skills; comfortable leading technical discussions with clinicians and engineers and presenting to senior executives • Excellent scientific writing skills; we may publish studies based on novel models or methods • A Master’s degree from an accredited college or university in a related area of Data Science, Statistics, Computer Science, Mathematics, Economics, or Information Technology. PhD preferred. Preferred: • Familiarity with major commercial data platforms, including Google cloud AI solutions. • Prior experience in regulated environments or deploying clinical decision support tools. • Demonstrated ability to leverage data visualization tools and software to present advanced analytics that are easy to interpret and spot patterns, trends, and correlations • Aptitude in other programing languages like R, SAS, JavaScript Why join this team? • High-impact work across products and markets; you have the opportunity to meaningfully improve patient outcomes and healthcare delivery in the United States in this role • Fully remote, collaborative team. • Opportunity to define production ML standards. • Clear ownership of end-to-end model lifecycle and opportunity to mentor others.

Responsibilities

• • Machine learning model garden used to create predictive analytics-based data products in healthcare.
• • Monitoring and surveillance of state of art research in machine learning in relation to healthcare and clinical AI.
• Incorporating innovation as appropriate into our ML garden and solutions.
• • Thought leaders support for ML Ops, including containerization, model serving, performance tuning, rollout strategies, and observability (drift, performance, alerts).
• • Model governance: reproducibility, versioning, bias/fairness checks, and audit-ready documentation.
• • Integration of advanced analytics and machine learning models into business products and services including business intelligence dashboards and real-time analytics.
• • Curation of data sets from Quest and non-Quest data sources in support of deriving business insights driven by advanced analytics.
• • Cross-functional partnership with clinicians and product owners to define outcomes, acceptance criteria, and validation plans.

Qualifications

• Required: • 2+ years evidence gaining deep knowledge about advanced machine learning concepts, new model architectures as well as research-level evaluation of promising model designs and architectures.
• • 5+ years relevant experience with Python and SQL; production-grade code and testing practices.
• • Practical experience with model serving and monitoring, CI/CD for ML, and feature pipeline orchestration.
• • Experience working with healthcare data (labs, EHR, claims) and familiarity with PHI handling/HIPAA considerations.
• • Excellent statistics, model evaluation, and pragmatic approach to validation.
• • Excellent communication and problem‑solving skills; comfortable leading technical discussions with clinicians and engineers and presenting to senior executives • Excellent scientific writing skills; we may publish studies based on novel models or methods • A Master’s degree from an accredited college or university in a related area of Data Science, Statistics, Computer Science, Mathematics, Economics, or Information Technology.
• Preferred: • Familiarity with major commercial data platforms, including Google cloud AI solutions.
• • Prior experience in regulated environments or deploying clinical decision support tools.