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About This Role
ABOUT THIS POSITION
Designs, develops and programs methods, processes, and systems to consolidate and analyze unstructured, diverse “big data” sources to generate actionable insights and solutions for client services and product enhancement. Interacts with product and service teams to identify questions and issues for data analysis and experiments. Develops and codes software programs, algorithms and automated processes to cleanse, integrate and evaluate large datasets from multiple disparate sources. Identifies meaningful insights from large data and metadata sources; interprets and communicates insights and findings from analysis and experiments to product, service, and business managers. Incumbents whose primary role is technical and focused on data storage, warehousing and systems architecture should be matched to Database Engineering or Storage Engineering. Incumbents whose focus is the quantitative analysis of complex business problems and issues using data from internal and external sources to provide insight to decision\-makers should be matched to Business Intelligence. Incumbents whose primary role is technical and focused on designing and building system\-generated reports, reporting tools and dashboards for data generation should be matched to Data Informatics. Incumbents whose focus is primarily on experimental design and advanced or complex statistical analysis and modeling of datasets should be matched to Statistician/Mathematician. This is a product engineering role in which employees work with multiple types of business data. Incumbents whose focus is primarily on analysis and modeling of financial, marketing or pricing data should be matched to Finance, Market Research or Pricing as appropriate. May be internal operations\-focused or external client\-focused, working in conjunction with Professional Services and outsourcing functions.WHAT YOU'LL DO
- Lead the design, development, and implementation of sophisticated machine learning models and predictive analytics solutions to solve critical business problems.
- Mentor and guide junior data scientists, fostering a culture of excellence and continuous learning within the team.
- Collaborate with cross\-functional teams (engineering, product, business stakeholders) to define data science project requirements, scope, and deliverables.
- Conduct in\-depth data exploration, analysis, and visualization to identify trends, patterns, and anomalies, presenting findings clearly and concisely to diverse audiences.
- Develop and maintain robust data pipelines and infrastructure in collaboration with data engineers to ensure data quality, accessibility, and integrity.
- Stay abreast of the latest advancements in data science, machine learning, and artificial intelligence, evaluating and recommending new technologies and methodologies.
- Contribute to the strategic direction of Waystar's data science initiatives, identifying opportunities for innovation and impact.
- Champion data\-driven decision\-making throughout the company, educating and influencing stakeholders on the power of data science.
- Evaluate and select appropriate statistical methods and machine learning algorithms for various business challenges, ensuring rigor and validity.
WHAT YOU'LL NEED
- Master's or Ph.D. in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
- 8\+ years of progressive experience in data science, with a proven track record of delivering impactful data\-driven solutions in a fast\-paced environment.
- Expert\-level proficiency in Python and/or R for data manipulation, statistical analysis, and machine learning model development.
- Extensive experience with various machine learning techniques (e.g., supervised, unsupervised, reinforcement learning, deep learning) and their practical applications.
- Strong understanding of statistical modeling, experimental design, hypothesis testing, and causal inference.
- Proficiency in SQL for data querying and manipulation, with experience working with large\-scale datasets.
- Experience with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Spark, Hadoop) is highly desirable.
- Excellent communication, presentation, and interpersonal skills, with the ability to explain complex technical concepts to non\-technical stakeholders.
- Demonstrated ability to lead projects, mentor team members, and drive successful outcomes.
- Prior experience in the healthcare technology or financial services industry is a plus.
ABOUT WAYSTAR
Through a smart platform and better experience, Waystar helps providers simplify healthcare payments and yield powerful results throughout the complete revenue cycle.
Waystar’s healthcare payments platform combines innovative, cloud\-based technology, robust data, and unparalleled client support to streamline workflows and improve financials so providers can focus on what matters most: their patients and communities. Waystar is trusted by 1M\+ providers, 1K\+ hospitals and health systems, and is connected to over 5K commercial and Medicaid/Medicare payers. We are deeply committed to living out our organizational values: honesty; kindness; passion; curiosity; fanatical focus; best work, always; making it happen; and joyful, optimistic \& fun.
Waystar products have won multiple Best in KLAS® or Category Leader awards since 2010 and earned multiple \#1 rankings from Black Book™ surveys since 2012\. The Waystar platform supports more than 500,000 providers, 1,000 health systems and hospitals, and 5,000 payers and health plans. For more information, visit waystar.com or follow @Waystar on Twitter.
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.
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Waystar, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Waystar AI Hiring
Waystar has 1 open AI role right now. They're hiring across Data Scientist. Based in Lehi, UT, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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