Applied Scientist, Patient Intelligence

About Bask Health

Bask Health is a software platform that enables entrepreneurs, doctors, and healthcare businesses to launch, manage, and grow direct-to-consumer telehealth businesses.

Our platform brings together patient experiences, clinical workflows, pharmacy operations, and commerce tools in one place.

We're an entrepreneurial, product-focused engineering team that values ownership, technical excellence, and building software that solves real problems. Engineers work closely with product teams and company leadership, take features from idea to production, and have a direct role in shaping the platform.

Engineering at Bask is AI-first and builder-led. We use tools like Cursor and Claude Code throughout development while maintaining high standards for reliability, testing, and software quality.

About the Role

We're looking for an Applied Scientist, Patient Intelligence to develop machine learning models and data-driven capabilities that help Bask Health better understand patient experiences and improve how our platform supports patients throughout their telehealth journeys.

Bask Health provides the technology behind direct-to-consumer telehealth businesses, connecting patient onboarding, questionnaires, clinical workflows, pharmacy coordination, and ongoing patient experiences. These interactions generate operational and behavioral information that can help us identify friction, improve workflows, and develop more intelligent product experiences.

You'll apply machine learning, statistical modeling, experimentation, and data analysis to identify meaningful patterns and build models that support better product decisions and patient experiences.

This is a hands-on applied science role focused on solving practical problems with data. You'll work with structured datasets, event data, application metrics, and approved patient-related information to develop predictive models, segmentation approaches, and intelligent features.

Your work may include understanding onboarding completion, identifying sources of patient drop-off, predicting workflow abandonment, analyzing engagement patterns, and developing models that help product teams improve the patient journey.

You'll collaborate with product managers, data specialists, machine learning engineers, and software engineers to define problems, design experiments, evaluate models, and translate findings into production-ready capabilities.

You'll be responsible for selecting appropriate modeling approaches, establishing reliable evaluation methods, and communicating the limitations of your findings. You'll help distinguish meaningful patterns from misleading correlations and ensure that model performance is assessed using representative data.

Because Bask operates in healthcare-related environments, responsible data use is essential. You'll work within approved privacy and security requirements, carefully evaluate potential bias, and ensure that patient intelligence systems support operational and product improvements without independently making clinical decisions.

This role is ideal for an applied scientist who enjoys combining rigorous analysis with practical product development and wants to see their work improve real software experiences.

What You'll Do

• 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

• 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.
• Familiarity with patient engagement analytics or digital health product metrics.
• Experience analyzing onboarding funnels, user journeys, and retention behavior.
• Experience building propensity models, churn models, or abandonment prediction systems.
• Familiarity with behavioral segmentation and clustering techniques.
• Experience with time-series analysis or longitudinal behavioral data.
• Familiarity with causal inference and uplift modeling.
• Experience designing and analyzing product experiments.
• Knowledge of scikit-learn, PyTorch, TensorFlow, XGBoost, or comparable ML frameworks.
• Experience with experiment tracking and model management tools.
• Familiarity with feature stores and production ML pipelines.
• Experience deploying models through APIs or cloud-based services.
• Familiarity with AWS or comparable cloud platforms.
• Experience working with event-based application data.
• Understanding of model drift detection and production performance monitoring.
• Experience applying natural language processing to approved text data.
• Familiarity with large language models and modern AI development techniques.
• Understanding of HIPAA and healthcare data privacy considerations.
• Experience evaluating fairness and bias in predictive models.
• Experience using AI-assisted development tools such as Cursor or Claude Code.