Senior AI Data Scientist

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🔥 6 minutes ago

🏈 Alabama, Arizona, +18 more states – Remote

🏈 Alabama – Remote 🌵 Arizona – Remote 🐊 Florida – Remote 🥔 Idaho – Remote 🌪️ Kansas – Remote 🦞 Maine – Remote 🌶️ New Mexico – Remote 🌲 North Carolina – Remote 🌪️ Oklahoma – Remote 🚗 Michigan – Remote 🎸 Mississippi – Remote 🏰 Missouri – Remote 🔔 Pennsylvania – Remote 🌴 South Carolina – Remote 🎸 Tennessee – Remote 🤠 Texas – Remote ⛷️ Utah – Remote ⚔️ Virginia – Remote ⛰️ West Virginia – Remote 🧀 Wisconsin – Remote

⏰ Full Time

🟠 Senior

📊 Data Scientist

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Knowtion Health

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2008

⚕️ Healthcare Insurance

💳 Fintech

☁️ SaaS

💰 Private Equity Round on 2020-09

Healthcare Insurance • Fintech • SaaS

Knowtion Health is a company that assists hospitals in handling denied and complex claims, recovering low balance accounts, and providing defense against payer audits. By leveraging advanced technology and a team of experts, Knowtion Health aims to maximize revenue and improve patient experiences. Their services include addressing clinical denials and managing various challenging payer classes, positioning them as a strong advocate for their clients in the healthcare sector.

📋 Description

• 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

• 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

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