Lead and optimize bioprocess development for plasmid DNA and protein biologics, ensuring compliance with regulatory standards while driving technology innovation and coordinating technical transfers across cross-functional teams.

Title: Manager, Data Scientist - Eso Location: Pittsburgh United States Job Description: Category Laboratory Location Pittsburgh, Pennsylvania Job function Operations Job family Laboratory Operations Shift Day Employee type Regular Full-Time Work mode Remote Job 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. Fully remote, minimal travel required; strong emphasis on hands‑on production experience and pragmatic problem solving. 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. Qualifications: - 5+ 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. - Excellent communication and problem‑solving 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. Preferred - Familiarity with feature stores and SHAP/interpretability tooling. - 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 58948 Quest Diagnostics honors our service members and encourages veterans to apply. While we appreciate and value our staffing partners, we do not accept unsolicited resumes from agencies. Quest will not be responsible for paying agency fees for any individual as to whom an agency has sent an unsolicited resume. Equal Opportunity Employer: Race/Color/Sex/Sexual Orientation/Gender Identity/Religion/National Origin/Disability/Vets or any other legally protected status.

• Responsible for developing, implementing, and optimizing advanced analytics and machine learning models to support data-driven decision-making across the organization • Applies statistical analysis, predictive modeling, and data exploration techniques to solve business problems, generate insights, and improve performance across functions such as marketing, operations, and risk • Identifies and evaluates data sources and collaborates with BI engineers to automate data collection processes • Performs preprocessing, cleansing, and validation of structured and unstructured datasets • Analyzes complex datasets to identify trends, patterns, and actionable insights • Develops and presents data visualizations and reports to communicate findings to stakeholders • Proposes data-driven solutions and strategies to address business challenges • Collaborates with cross-functional teams including engineering, analytics, and product development • Develop, implement, and maintain statistical and machine learning models to address business challenges and improve decision-making • Ensures data integrity, accuracy, and consistency across analyses

• Structure, lead and develop the team, taking responsibility for selecting new talent, managing team morale and conducting 1:1s, continuous feedback and performance reviews; • Act as a strategic partner, negotiating scope, deadlines and priorities with stakeholders and senior leadership; • Define the technical vision and roadmap for the data platform, aligning architecture with business objectives; • Prioritize the backlog together with Product Managers, ensuring predictability and quality of deliveries; • Manage incidents and risks, coordinating war rooms and leading post-mortems with a focus on continuous improvement; • Make architecture and stack decisions, evaluating trade-offs between cost, performance and market solutions (build vs. buy); • Establish engineering standards, such as code guidelines, design reviews and reusable frameworks; • Solve complex technical challenges and perform critical code reviews on strategic projects; • Ensure governance and compliance, overseeing LGPD (Brazilian Data Protection Law), security and cost optimization (FinOps).