Senior Data Engineer Jobs in Midvale, UT (NOW HIRING)

We are looking for an experienced Data Scientist who has previously supported Healthcare software applications. The role involves solving technical, data‑driven Healthcare problems using computer science, mathematics, predictive modeling and statistical methods, and knowledge. The data scientist will interact with teams from Account Management to Application Engineering and R&D to conduct detailed analysis and experimentation to maximize the utility of predictive modeling, analytic, and machine learning across Waystar’s product line. The focus is on extending machine learning, predictive modeling, and analytic components to provide up‑to‑date intelligence to Healthcare providers, maximizing outcomes.

• Work closely with Application Engineering, Product Management, and Operational teams in designing, experimenting with, and implementing machine learning and analytical systems applied to design information and user behavior.
• Collaborate with Application Engineering teams to gather and process data and surface analytically‑based features in core products.
• Work on groundbreaking new applications of machine learning and analytic technology to Healthcare, producing quantitative, justifiable results to guide feature planning.
• Translate real‑world Healthcare problems to mathematical frameworks.
• Partner with Product Management, Marketing, and Sales as needed to promote sales and incorporate market and customer feedback.
• Engage in data exploration, hypothesis creation (from business and product goals), testing algorithms, scaling to large data‑sets, and validating results.
• Understand, organize, and communicate root causes of problems and successes succinctly.

• Complete familiarity with statistical and machine‑learning techniques including classification, regression, dimensionality reduction, clustering, and various multivariate methods.
• Complete familiarity with empirical techniques for estimating machine‑learning model performance, such as hold‑out sets, cross‑validation, and leave‑one‑out testing.
• Understanding of algorithmic complexity and scaling.
• Demonstrated competency in R or Python predictive modeling.
• Demonstrated competency in RDBMS (e.g., SQL Server).
• Ability to code in one or more general‑purpose programming languages (C#, Java, etc.).
• Quick‑learner with the ability to multi‑task in a fast‑paced environment.
• Outstanding presentation and communication skills, able to engage all levels of the business.
• Strong analytical, problem‑solving, and written communication skills.
• Proficiency in Microsoft Office applications.
• Detail‑oriented.
• Master’s degree or higher in Computer Science, Statistics, or Mathematics is preferred.
• Aptitude for medical informatics is preferred.

• Competitive total rewards (base salary + bonus, if applicable).
• Customizable benefits package (three medical plans with Health Savings Account company match).
• Generous paid time off for non‑exempt team members, starting with three weeks + 13 paid holidays, including two personal floating holidays.
• Flexible time off for exempt team members + 13 paid holidays.
• Paid parental leave (including maternity and paternity leave).
• Education assistance opportunities and free LinkedIn Learning access.
• Free mental health and family planning programs, including adoption assistance and fertility support.
• 401(k) program with company match.
• Pet insurance.
• Employee resource groups.

Waystar is proud to be an equal‑opportunity workplace. We celebrate, value, and support diversity and inclusion. Qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, national origin, disability status, genetics, marital status, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.