Principal Applied Scientist

Role Overview

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.

What You Will Do

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.

Why It Might Be a Fit

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.

Requirements

• 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

Benefits

• Medical, dental and vision coverage with low deductible & copay
• Life insurance
• Short and long-term disability
• Paid Parental Leave
• 401(k) + match
• Employee Stock Purchase Plan
• Generous Paid Time Off – accrued based on years of service
• 10 paid company holidays
• Tuition reimbursement
• Flexible Spending Account
• Employee Assistance Program
• Sick time benefits – for eligible employees, one hour of sick time for every 30 hours worked, up to a maximum accrual of 40 hours per calendar year

Originally posted on Himalayas