[Hiring] Applied Scientist @Bask Health

Role Description

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.

• Apply machine learning, statistical modeling, experimentation, and data analysis to identify meaningful patterns and build models that support better product decisions and patient experiences.
• Work with structured datasets, event data, application metrics, and approved patient-related information to develop predictive models, segmentation approaches, and intelligent features.
• 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.
• Ensure responsible data use within approved privacy and security requirements, carefully evaluate potential bias, and support operational and product improvements without independently making clinical decisions.
• Own assigned applied science projects from problem definition through experimentation, evaluation, and implementation support.

Qualifications

• 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.
• Proficiency with SQL and working with structured datasets.
• Strong analytical thinking and problem-solving skills.
• Excellent attention to detail and documentation practices.

Requirements

• 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.
• Ability to translate product or business questions into measurable analytical problems.
• Experience communicating technical findings to nontechnical stakeholders.
• Understanding of responsible data use and privacy-conscious analytical practices.

Benefits

• Opportunity to work on impactful projects that improve real software experiences.
• Collaborative and entrepreneurial work environment.
• Access to advanced tools and technologies in applied machine learning.