Applied Scientist – Patient Intelligence

Bask Health

. Develop machine learning models that support patient experience and operational intelligence across Bask Health.

Posted 11/12/2026full-timeRemote • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and evaluating machine learning models, particularly in healthcare applications, with a strong focus on patient engagement and operational intelligence. Proficient in statistical analysis, data cleaning, and feature engineering, while effectively communicating technical findings to diverse stakeholders.

Highest-signal resume keywords

Applied Machine LearningPython ProgrammingStatistical AnalysisModel Evaluation MetricsData Cleaning and Transformation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning ModelsPredictive ModelingFeature EngineeringSQLExploratory Data AnalysisA/B TestingStatistical SignificanceModel Training WorkflowsBehavioral SegmentationNatural Language Processing

Soft Skills

Analytical ThinkingProblem-SolvingAttention to DetailCommunication SkillsProject Ownership

Tools & Technologies

Scikit-learnPyTorchTensorFlowXGBoostAWSAPIsExperiment Tracking ToolsVersion Control SystemsCloud-Based ServicesFeature Stores

Industry Keywords

Healthcare Data PrivacyPatient Engagement AnalyticsOperational IntelligenceDigital Health ProductsModel Drift Detection

Tech Stack

Tools & technologies

AWSCloudPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Develop machine learning models that support patient experience and operational intelligence across Bask Health.
Analyze patient journey data to identify friction points, behavioral patterns, and opportunities for product improvement.
Build predictive models for approved patient engagement and workflow outcomes.
Develop models that help identify potential onboarding abandonment and incomplete workflows.
Analyze questionnaire completion patterns and other patient interaction signals.
Create segmentation models that help product teams understand different patient experience patterns.
Design and implement machine learning approaches for classification, prediction, and behavioral analysis.
Develop feature engineering pipelines using structured application and event data.
Build reproducible training, validation, and evaluation workflows.
Evaluate model performance using appropriate statistical and machine learning metrics.
Design experiments to assess the effectiveness of intelligent product features.
Collaborate with product managers to define measurable research questions and success criteria.
Analyze experiment results and communicate actionable findings.
Develop methods for identifying changes in patient engagement and workflow behavior.
Work with engineers to integrate approved machine learning capabilities into production applications.
Build prototypes that demonstrate the practical value of new modeling approaches.
Evaluate baseline statistical methods against more complex machine learning models.
Investigate model errors, data quality problems, and unexpected prediction behavior.
Monitor model performance and identify potential data or concept drift.
Develop dashboards, reports, and analytical outputs that communicate model findings.
Collaborate with engineering teams to improve data collection and event instrumentation.
Document modeling assumptions, evaluation methodologies, and known limitations.
Evaluate models for potential bias, inconsistent performance, and unintended consequences.
Ensure appropriate handling of sensitive patient and operational information.
Support responsible experimentation and controlled deployment of predictive features.
Participate in technical discussions, research reviews, and engineering planning.
Stay current with relevant advances in applied machine learning and statistical modeling.
Own assigned applied science projects from problem definition through experimentation, evaluation, and implementation support.

Requirements

What you’ll need

3+ years of professional experience in applied machine learning, data science, applied research, or a closely related technical field.
Strong programming skills in Python or comparable languages used for machine learning and statistical analysis.
Experience developing and evaluating machine learning models using real-world datasets.
Strong understanding of supervised learning, classification, regression, and predictive modeling.
Familiarity with statistical analysis and experimental design.
Experience using Python data science and machine learning libraries.
Proficiency with SQL and working with structured datasets.
Experience cleaning, transforming, and validating data for analysis and modeling.
Understanding of feature engineering and model training workflows.
Familiarity with appropriate model evaluation metrics and validation techniques.
Ability to identify overfitting, data leakage, and other common modeling problems.
Experience conducting exploratory data analysis.
Understanding of statistical significance, confidence intervals, and experimental uncertainty.
Familiarity with A/B testing or controlled experimentation.
Ability to translate product or business questions into measurable analytical problems.
Experience communicating technical findings to nontechnical stakeholders.
Familiarity with reproducible research and version-controlled development.
Understanding of production machine learning considerations.
Ability to collaborate with software engineers on model integration.
Experience evaluating data quality and identifying inconsistent or incomplete records.
Understanding of responsible data use and privacy-conscious analytical practices.
Ability to recognize limitations in observational data and model predictions.
Strong analytical thinking and problem-solving skills.
Excellent attention to detail and documentation practices.
Ability to independently own applied research and modeling projects.
Nice to Have: Experience developing machine learning models for healthcare, telehealth, or patient-facing products.
Nice to Have: Familiarity with patient engagement analytics or digital health product metrics.
Nice to Have: Experience analyzing onboarding funnels, user journeys, and retention behavior.
Nice to Have: Experience building propensity models, churn models, or abandonment prediction systems.
Nice to Have: Familiarity with behavioral segmentation and clustering techniques.
Nice to Have: Experience with time-series analysis or longitudinal behavioral data.
Nice to Have: Familiarity with causal inference and uplift modeling.
Nice to Have: Experience designing and analyzing product experiments.
Nice to Have: Knowledge of scikit-learn, PyTorch, TensorFlow, XGBoost, or comparable ML frameworks.
Nice to Have: Experience with experiment tracking and model management tools.
Nice to Have: Familiarity with feature stores and production ML pipelines.
Nice to Have: Experience deploying models through APIs or cloud-based services.
Nice to Have: Familiarity with AWS or comparable cloud platforms.
Nice to Have: Experience working with event-based application data.
Nice to Have: Understanding of model drift detection and production performance monitoring.
Nice to Have: Experience applying natural language processing to approved text data.
Nice to Have: Familiarity with large language models and modern AI development techniques.
Nice to Have: Understanding of HIPAA and healthcare data privacy considerations.
Nice to Have: Experience evaluating fairness and bias in predictive models.
Nice to Have: Experience using AI-assisted development tools such as Cursor or Claude Code.