Senior AI Data Scientist

Knowtion Health

Senior AI Data Scientist managing end-to-end data science tasks within a healthcare company. Focusing on model building, validation, and operational maintenance in a competitive environment.

Posted 7/24/2026full-timeRemote • Alabama, Arizona, Florida, Idaho, Kansas, Maine, Mississippi, Missouri, Montana, New Mexico, North Carolina, Oklahoma, Pennsylvania, South Carolina, Tennessee, Texas, Utah, Virginia, West Virginia, Wisconsin • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and validating statistical models, managing data pipelines, and implementing MLOps practices to ensure model performance and reliability in production environments. Proficient in Python, SQL, and statistical methodologies to address complex revenue-cycle challenges.

Highest-signal resume keywords

Model Building And ValidationMLOps ImplementationStatistical AnalysisData Pipeline ManagementPython And SQL Proficiency

ATS Keywords

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Hard Skills

Model ValidationStatistical ModelingExperimental DesignData CleansingFeature DesignClass Imbalance HandlingCalibration TechniquesPerformance-Aware SQLData Leakage PreventionOverfitting And Regularization

Soft Skills

Collaboration With Subject-Matter ExpertsDocumentation Of Business Requirements

Tools & Technologies

PythonPandasNumPyScikit-LearnGitEHR Systems

Industry Keywords

Revenue Cycle ManagementMLOpsData QualityModel MonitoringDrift Detection

Tech Stack

Tools & technologies

NumpyPandasPythonScikit-LearnSQL

About the role

Key responsibilities & impact

Frame ambiguous revenue-cycle problems as tractable modeling problems and own them end to end such as claim and invoice viability scoring, denial and underpayment prediction, revenue/ARR prediction, and work prioritization from hypothesis to a validated, monitored result in production
Build and validate models with statistical rigor: thoughtful feature design, sound handling of missingness and class imbalance, calibration, honest evaluation against simple baselines, and a clear treatment of uncertainty
Design, build, and maintain the data pipelines feeding these models integrating with source collections, billing, and EHR systems and internal data stores, and handling data collection, cleansing, field mapping, and payer/plan normalization with a bias toward reliability and data quality
Establish and own MLOps practice: model monitoring and drift detection (e.g., population stability), calibration and threshold selection, benchmarking against simple baselines, explainability (e.g., SHAP and interpretable coefficients), experiment and version tracking, and model documentation such as model cards
Partner with subject-matter experts to encode business-rule and heuristic logic alongside statistical models where that produces a more accurate or more defensible result
Gather and document business requirements for revenue-cycle initiatives, including workflows, decision points, stakeholder objectives, operational constraints, success criteria, data availability, and underlying business assumptions, and maintain traceability as those requirements evolve.

Requirements

What you’ll need

Several years building and validating models and operating them in production
A degree in a quantitative field such as statistics, computer science, mathematics, or data science, or equivalent demonstrated ability
A strong statistical foundation experimental design, inference, evaluation of imbalanced real-world data, and knowing when a result is real versus an artifact of how it was measured
Deep, genuine fluency in the fundamentals: train/test discipline, overfitting and regularization, data leakage, class imbalance, calibration, and how to evaluate a model honestly
Strong production Python (pandas, NumPy, scikit-learn; deep-learning frameworks a plus) and strong SQL, including stored procedures and performance-aware queries against large tables, with comfort using Git and reproducible workflows
Hands-on MLOps: monitoring, drift, experiment tracking, versioning, and what it takes to keep a model healthy in production
Comfort owning the data pipeline, not just the model source-system integration, scheduling, and data-quality controls.

Benefits

Comp & perks

medical
dental
vision
life insurance
short term disability
long term disability
paid holidays
401k
generous PTO policy