Principal Applied Scientist

Overview

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

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

This role requires deep technical expertise and the executive presence to engage directly with C-suite stakeholders, shaping the AI strategy for the finance organization and influencing high-impact financial decisions. The Principal AI Research Scientist will also mentor team members, partner across Finance, Sales, Product, Engineering, and Business Operations, and contribute to the creation of scalable, production-ready data and AI solutions.

Job Roles And Responsibilities

Lead the research, design, development, and maintenance of advanced statistical and machine learning models, including forecasting, risk scoring, anomaly detection, optimization, and financial trend modeling on huge amounts of healthcare data.
Partner closely with Finance, Sales, Product, Engineering, and Business Operations leaders to identify opportunities to leverage company data for financial insights, revenue growth, cost reduction, efficiency optimization, and risk mitigation. Demonstrate strong executive presence by communicating complex AI/ML concepts clearly and persuasively to C-suite and senior leadership.
Research, prototype, and evaluate new AI/ML methodologies, particularly in traditional ML, predictive modeling, and anomaly detection, to solve complex financial and operational problems that have direct top-line and bottom-line financial impact.
Write high-quality, production-ready code to enable scalable data and ML solutions that drive financial transparency, improve forecasting accuracy, and identify key cost/value levers.
Partner with AI Engineering, Enterprise Data, FinTech, DevOps, Infrastructure, and Cloud teams to support deployment pipelines, model integration, and monitoring frameworks.
Develop processes and tools for model performance tracking, drift detection, model risk management, and financial data quality assurance.
Serve as a cross-functional leader who collaborates, coordinates, and communicates effectively across disciplines; support onboarding and mentorship for new team members.
Ensure compliance with HIPAA and other regulatory frameworks relevant to financial and operational data in healthcare.
Champion Claritev’s core values and competencies.
Please note due to the exposure of PHI sensitive data — this role is considered to be a High Risk and Privileged Role.

Company:

Claritev

Qualifications:

Ph.D. or M.S. in Computer Science, Statistics, Applied Mathematics, Data Science, Financial Engineering, 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, ideally in finance, healthcare, or other regulated sectors.
Proven experience in financial forecasting, risk modeling, anomaly detection, large-scale data analysis, and insight generation.
Experience working within model risk management, compliance frameworks, or regulated data environments.
Strong proficiency in Python and ML/deep learning frameworks (e.g., PyTorch, TensorFlow).
Experience with big-data technologies and scalable model deployment in the cloud environment.
Experience with Generative AI or agent frameworks (e.g., LangChain, LangGraph, Autogen) is a nice to have, but the primary focus is predictive ML.
Experience designing or implementing model evaluation pipelines, including accuracy, reliability, explainability, and latency metrics.
Strong foundation in ML theory, statistics, time-series modeling, and anomaly detection.
Domain knowledge in corporate finance and healthcare is strongly preferred.
A product-oriented mindset with the ability to align scientific rigor to business impact.
Exceptional communication, stakeholder management, and cross-functional collaboration skills; ability to influence senior and executive level stakeholders.

Educational level:

Master Degree

Level of experience (years):

Senior (5+ years of experience)

Tagged as: Data Analysis, Data Mining, Industry, Machine Learning, Master Degree, United States