ABOUT THIS POSITION

The Data Science focus specializes in extracting insights and solving complex problems using advanced statistical analysis, machine learning, and predictive modeling. Collects, cleans and analyses large datasets to uncover patterns and make data-driven decisions. Responsibilities include developing algorithms, building predictive models, and communicating findings through data visualization. This role often requires a PhD in a statistics field. This job family is in the professional level category and works independently with flexibility and authority over daily work priorities. Incumbents in this job level category may provide work direction to other employees but would not typically have the authority to discipline or reward other employees. There are different levels available that provide clarity on contribution expectations based on knowledge, application, problem solving, and collaboration.

WHAT YOU'LL DO

* Clean, transform, and analyze large, complex datasets to identify trends, patterns, and insights relevant to business objectives.
* Assist in the development and implementation of statistical models, machine learning algorithms, and predictive analytics solutions.
* Prepare and present findings and recommendations to technical and non-technical stakeholders in a clear and concise manner.
* Collaborate with engineering, product, and business teams to understand data needs and translate them into actionable data science initiatives.
* Develop and maintain data visualizations and dashboards to monitor key performance indicators and track model performance.
* Participate in code reviews and contribute to maintaining high-quality, well-documented data science code.
* Stay up-to-date with the latest advancements in data science, machine learning, and artificial intelligence.
* Support data governance and data quality initiatives.

WHAT YOU'LL NEED

* Bachelor's degree in Computer Science, Statistics, Mathematics, Economics, or a related quantitative field. Master's degree preferred.
* 1-3 years of professional experience in a data science, data analysis, or analytical role.
* Proficiency in Python or R for data analysis and statistical modeling.
* Strong understanding of statistical concepts, hypothesis testing, and experimental design.
* Experience with SQL for data extraction and manipulation.
* Familiarity with machine learning algorithms (e.g., regression, classification, clustering) and their applications.
* Experience with data visualization tools (e.g., Tableau, Power BI, Matplotlib, Seaborn).
* Excellent problem-solving skills and a strong analytical mindset.
* Ability to communicate complex data findings effectively to diverse audiences.
* Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Spark) is a plus.

WAYSTAR PERKS

• Competitive total rewards (base salary + bonus, if applicable)
• Customizable benefits package (3 medical plans with Health Saving Account company match)
• We offer generous paid time off for our non-exempt team members, starting with 3 weeks + 13 paid holidays, including 2 personal floating holidays. We also offer flexible time off for our exempt team members + 13 paid holidays
• Paid parental leave (including maternity + 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.