Senior AI Data Scientist — End-to-End ML & MLOps (Remote)

Knowtion Health; United States; Remote; USD 150,000 - 210,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Life insurance

Short-term disability

Long-term disability

Paid holidays

401k

Generous PTO

Job summary

Knowtion Health is hiring a Senior AI Data Scientist for a remote role in the United States. You will own models end to end—from framing problems to deploying data pipelines and monitoring in production.

The position emphasizes both data science and engineering, with high standards for model defensibility and operational reliability. You will frame revenue-cycle problems, build robust models, establish MLOps practices, and work with experts to translate business requirements into scalable,

Qualifications

• Several years building and validating models in production.
• Degree in a quantitative field such as statistics, CS, math, or data science, or equivalent demonstrated ability.
• Strong foundation in experimental design, imbalance handling, calibration, and honest evaluation.

Responsibilities

• Frame revenue-cycle problems as tractable modeling problems and own them end to end.
• Build, validate, and monitor models with statistical rigor in production.
• Design, build, and maintain data pipelines feeding these models with source data and data quality controls.
• Establish and own MLOps practices: monitoring, drift detection, and versioning.
• Collaborate with domain experts to encode business rules alongside models.

Skills

Python

SQL

Pandas

NumPy

scikit-learn

SHAP

Education

Bachelor's degree in statistics, computer science, mathematics, or data science

Tools

Git

MLflow

Airflow

Job description

Knowtion Health is hiring a Senior AI Data Scientist for a remote role in the United States. You will own models end to end—from framing problems to deploying data pipelines and monitoring in production.

The position emphasizes both data science and engineering, with high standards for model defensibility and operational reliability. You will frame revenue-cycle problems, build robust models, establish MLOps practices, and work with experts to translate business requirements into scalable,