Sr. Actuarial Data Scientist (Hybrid - Secaucus, NJ)

Pay Range:

$105k/$120k 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: Day 1 Medical, supplemental health, dental & vision for FT employees who work 30+ hours Best-in-class well-being programs Annual, no-cost health assessment program Blueprint for Wellness® healthyMINDS mental health program Vacation and Health/Flex Time 6 Holidays plus 1 "MyDay" off FinFit financial coaching and services 401(k) pre-tax and/or Roth IRA with company match up to 5% after 12 months of service Employee stock purchase plan Life and disability insurance, plus buy-up option Flexible Spending Accounts Annual incentive plans Matching gifts program Education assistance through MyQuest for Education Career advancement opportunities and so much more! The Actuarial Data Scientist, provides individual contribution to the financial quantification of risk and opportunity across Quest's strategic initiatives. This role is accountable for embedding a forward-looking, financially disciplined perspective into our advanced analytics practice by exploring and synthesizing large, complex datasets; framing hypotheses and research approaches; and building, deploying, and monitoring sophisticated statistical models in line with actuarial standards and practices. This individual will combine strong business acumen with advanced quantitative methods and modern software engineering practices to translate complex business challenges into reliable, production-ready solutions. Key applications include developing the financial architecture for value-based care initiatives, pricing at-risk contracts, and modeling the ROI of new solutions/products including AI-driven. This role is a partner to functional leaders, shaping enterprise strategy by modeling the long-term financial impact of clinical, operational, and market trends. 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. Actuarial Modeling, Pricing, and Strategic Risk Analysis Design, development, and deployment of sophisticated financial and risk models to evaluate the profitability and risk profile of key business initiatives. Develop dynamic pricing and underwriting frameworks for a range of commercial applications, including value-based care and at-risk partnerships, using advanced mathematical and statistical techniques (e.g., GLM/GBM). Build complex, tested programs to run statistical tests on data, discover insights and relationships, and understand complex relationships across attributes. Apply stochastic modeling to forecast healthcare cost and utilization, translating these insights into actionable business strategies and applying reserving concepts (e.g., IBNR) and stress testing to financial plans. Incorporate findings and provide industry and competitor insights as part of model development and enhancement, ensuring all work aligns with relevant professional standards. Cloud Platforms, Data Engineering & MLOps for Financial Modeling Work with MLOps capabilities for the deployment, monitoring, and lifecycle management of financial and risk models in a cloud environment. Partner with enterprise data engineering to build robust, tested analytics pipelines for end-to-end use cases (data prep, modeling, validation, and automation). Champion software development best practices including source control, coding standards, and testing frameworks within the analytics team. Business Partnership & Financial Strategy Serve as a strategic partner to senior business leaders, translating complex models into clear financial implications and actionable recommendations. Quantify and communicate the potential ROI and risk drivers of major analytics initiatives, demonstrating a pragmatic approach to solution-selection and an understanding of when a non-AI approach is optimal. Enhance traditional financial planning and analysis (FP&A) with more dynamic, probabilistic forecasting methods. Leadership, Talent Development & Delivery Lead, mentor, and develop data scientists and analysts, instilling a culture of financial discipline, risk awareness, and technical excellence. Establish and reinforce best practices for the entire model lifecycle, from hypothesis generation to production monitoring. Education Bachelors or higher in Actuarial Science, Data Science, Statistics, Economics, Applied Mathematics, or a related quantitative discipline. Experience 2 - 4+ years of progressive experience in a role requiring sophisticated forecasting and quantitative risk modeling. Demonstrated experience formulating, approaching, and solving complex analytical problems using a quantitative, scientific approach. Proven experience having deployed and monitored predictive models in a production environment, partnering with business and engineering teams. Experience working with large, complex datasets using big data technologies and script. Technical & Quantitative Expertise Advanced programming proficiency with a strong preference for Python, applied to data access, scripting, statistical analysis, and system development. Strong experience with SQL and analytical data modeling. Deep understanding of long-term forecasting techniques and methods for modeling uncertainty. Proficiency with machine learning frameworks (e.g., Scikit-Learn, GLM/GBM libraries) and a proven ability to translate model outputs into financial impacts. Knowledge of MLOps, model governance, and production AI environments. Leadership & Professional Skills Excellent communication skills with the ability to influence senior leaders by articulating the "so what" behind complex models. Proven ability to deliver outcomes in a complex, matrixed organization. Preferred Qualifications Credentialed Actuary (ASA, FSA) or significant progress toward credentials. Advanced degrees with a focus on a quantitative, economics, or health-related field. Prior experience in healthcare, diagnostics, insurance, or life sciences. Direct experience designing or operating analytics and AI solutions on Google Cloud Platform (GCP).