Manager, Data Scientist - Eso

Quest Diagnostics Incorporated•Pittsburgh, PA

47d•$100,000 - $130,000•Remote

About The Position

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 the full spectrum of healthcare markets, including pharma, payers, integrated delivery networks and public health. Join HAS to build, validate and deploy 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 wellâdocumented models that impact patient care and downstream products. Fully remote, minimal travel required; strong emphasis on handsâon machine learning model development and deployment experience and pragmatic problem solving. Pay Range: $100,000 - $130,000 / year Salary offers are based on a wide range of factors including relevant skills, training, experience, education, and, where applicable, certifications obtained. Market and organizational factors are also considered. Successful candidates may be eligible to receive annual performance bonus compensation. Benefits Information: We are proud to offer best-in-class benefits and programs to support employees and their families in living healthy, happy lives. Our pay and benefit plans have been designed to promote employee health in all respects - physical, financial, and developmental. Depending on whether it is a part-time or full-time position, some of the benefits offered may include: Medical/Prescription Drugs Dental Vision Flexible Spending Accounts (FSAs) Supplemental Health Plans 401(k) Plan - Company match dollar-for-dollar up to 5% Employee Stock Purchase Plan (ESPP) Supplemental Life Insurance Dependent Life Insurance Short- and Long-Term Disability buy-up Blueprint for Wellness Emotional Well-Being Resources Educational Assistance Paid time off / Health Time Variable Compensation Plans Sales Incentive Plans

Requirements

2+ years demonstrated track record delivering ML models to production (architecture, deployment, and monitoring), preferably in the healthcare industry.
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.
Strong statistics, model evaluation, and pragmatic approach to validation, including the ability to articulate the bias-variance tradeoff in models to stakeholders.
Comfort with integrated generative AI tools in the model building and general coding workflow, with knowledge and strategies of both when to use and not use these tools.
Excellent communication and problemâsolving skills; comfortable leading technical discussions with clinicians and engineers and presenting to senior executives
A bachelor's degree from an accredited college or university in a related area of Data Science, Statistics, Computer Science, Mathematics, Economics, or Information Technology. Master's or PhD preferred.

Nice To Haves

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
Experience developing, deploying, and monitoring production models in the C3.ai platform
Excellent scientific writing skills; we may publish studies based on novel models or methods
Experience supporting the creation of SOWs and Contracts.
Aptitude in other programing languages like R, SAS, JavaScript

Responsibilities

End-to-end ML delivery: data curation, feature design, modelling, validation, CI/CD deployment, and monitoring.
Productionization: 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.