Principal Applied Scientist

At Claritev, our mission is to simplify healthcare workflows, improve transparency, and bend the healthcare cost curve. We believe that data, technology, and AI can fundamentally transform how healthcare operates by automating complex workflows, improving decision-making, and reducing unnecessary costs across the system.

By combining deep healthcare expertise with advanced analytics and AI, we help payers, providers, and employers operate more efficiently and deliver better outcomes for the people they serve. We are bold in our thinking, rigorous in execution, and committed to service excellence for every stakeholder. Our culture values innovation, accountability, diversity of thought, and collaboration. 

Join us as we accelerate our transformation into a leading technology and AI-driven company shaping the future of healthcare.

JOB SUMMARY

We are seeking a Principal Applied Scientist to lead the research, development, and deployment of advanced machine learning and AI systems that power Claritev’s next generation of healthcare products. This is a hands-on technical leadership role for an experienced applied scientist who thrives at the intersection of research innovation, real-world deployment, and measurable business impact.

You will lead the development of advanced analytics, statistical and machine learning models to support Claritev’s corporate data & analytics, forecasting, and risk-intelligence capabilities. This leader will own end-to-end machine learning initiatives—from problem definition through deployment—focused on forecasting, anomaly detection, risk modeling, model governance, and insight generation using large datasets within highly regulated environments.

In this role, you will work closely with Product, Engineering, and business leaders to translate cutting-edge research into scalable production solutions that improve transparency, reduce costs, and simplify healthcare operations. You will also serve as a technical thought leader and mentor, helping shape Claritev’s AI strategy and elevating the scientific rigor and innovation of the organization.

KEY RESPONSIBILITIES

• Design, train, validate, and test ML and statistical models, including risk modeling, time series analysis, optimization, prediction, anomaly detection, knowledge discovery, prescriptive recommendations, and other applications.
• Partner closely with Product Management, Engineering, and Operations to identify opportunities to leverage healthcare data sets for revenue generation, cost reduction, efficiency optimization, insights, and risk mitigation; translate these opportunities into technical solutions leveraging ML.
• Research and prototype new AI/ML methodologies to improve the cost effectiveness and quality of healthcare per dollar spent by our customers.
• Write production-ready code implementing models and feature pipelines fed by our data engineering teams and deployed at scale by our AI/ML Ops teams.
• Develop AI/ML observability for your models, including monitoring covariate shift and concept drift, adaptation/retraining strategies, and failure contingencies.
• At the intersection of agentic AI and classical ML, collaborate with AI engineers by ensuring that information is extracted and encoded for efficient and responsible use by LLMs. Participate in the core functionality of agentic AI.
• Communicate your work clearly through documentation and presentations.
• Mentor less-senior members of the team and oversee their work on joint projects.
• Ensure quality and compliance with governance and regulatory frameworks relevant to healthcare like HIPAA.
• Make healthcare more affordable and transparent for our customers while exhibiting Claritev's core values.

JOB REQUIREMENTS

• Ph.D. or M.S. in Computer Science, Statistics, Applied Mathematics, Data Science, or related STEM fields.
• 8+ years of industry experience in applied machine learning, advanced analytics, and predictive modeling with a demonstrated ability to deliver ML solutions from prototype to production.
• Strong knowledge in ML theory and foundational statistics. Deep understanding and experience in machine learning best practices including EDA, model selection, bias/variance tuning, validation, sensitivity analysis, dimensionality reduction, feature selection, interpretability/explainability, etc.
• Proven experience with classification & regression, time-series modeling, anomaly detection, large-scale data analysis, insight generation, and recommender algorithms.
• Strong proficiency in Python, SQL, deep learning frameworks (e.g., PyTorch), and other common ML libraries & frameworks.
• Experience with big data technologies and scalable model deployment in the cloud environment.
• Experience designing or implementing model evaluation pipelines, including accuracy, reliability, explainability, and latency metrics.
• Prefer experience with Foundation Models (e.g., LLMs) and agentic frameworks, but the primary focus of this role is predictive ML, including traditional models (e.g., GBTs, RFs, clustering, etc.) and larger models (BERT, deep learning, etc.).
• Prefer domain knowledge and experience working within environments involving risk management, compliance frameworks, or regulated data such as finance and healthcare.
• A product-oriented mindset that aligns theoretical rigor with business impact
• Exceptional communication, stakeholder management, and cross-functional collaboration skills; ability to influence senior and executive level stakeholders.