Remote Principal Data Scientist - ML & AI Strategy

Sedgwick; Detroit (MI); Remote; USD 140,000 - 210,000; Full time

Job summary

Sedgwick is seeking a senior data science leader to design and develop advanced statistical and machine learning models that improve claims outcomes and risk management across our enterprise analytics platform.

You will mentor data scientists, translate complex findings for business leaders, and collaborate with AI engineering teams to productionize models while driving governance and responsible analytics across Claims Operations, Finance, Risk and IT.

Qualifications

• Master’s or PhD in a quantitative field (Data Science, Statistics, Mathematics, CS, Economics)
• 8–12+ years of data science/analytics experience
• Deep expertise in ML algorithms and predictive analytics
• Strong programming in Python, R, or similar
• Experience deploying modeling pipelines in enterprise environments

Responsibilities

• Lead design and development of advanced ML models for claims outcomes, efficiency and risk management.
• Serve as technical authority for modeling initiatives including fraud detection and litigation risk modeling.
• Develop predictive models using structured and unstructured data from claims, medical records, and policy docs.
• Architect modeling pipelines leveraging gradient boosting, deep learning, NLP, and anomaly detection.
• Partner with AI Engineering to productionize models and integrate into enterprise platforms.
• Design feature engineering strategies and scalable pipelines.

Skills

Python

R

Data wrangling

Machine learning

Communication

Mentoring

Education

Master’s or PhD in a quantitative field

Tools

TensorFlow

PyTorch

SQL

Job description

Sedgwick is seeking a senior data science leader to design and develop advanced statistical and machine learning models that improve claims outcomes and risk management across our enterprise analytics platform.

You will mentor data scientists, translate complex findings for business leaders, and collaborate with AI engineering teams to productionize models while driving governance and responsible analytics across Claims Operations, Finance, Risk and IT.