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About This Role
Join the team shaping the future of healthcare! Medbridge is a dynamic software as a service company working with the country’s largest healthcare providers to build technology solutions helping patients get better faster, while decreasing the overall cost of care.
- 15 years serving Acute, Post-Acute, and Ambulatory settings.
- 4,000+ organizations, 360,000+ healthcare providers, and 15 million patients served annually.
- 9 of the top 10 largest hospitals and health systems, 9 of the top 10 largest private practices, and 6 of the top 10 largest home health agencies run on Medbridge.
Why work at Medbridge?
- We are mission driven. Our mission is clear, we want to help everyone move well, feel well, and live well.
- Medbridge's Educate is built in-house by 600+ named instructors, many of whom wrote the guidelines clinicians already follow.
- Medbridge's Care gives those clinicians home exercise programs, patient education, clinical pathways, remote therapeutic monitoring, and outcomes collection inside the workflows they already use
- You'll shape the Data Science practice. This is Medbridge's first dedicated data science role, reporting to a Head of Data & ML who is a statistician by training and still writes code. You will set the modeling standards the team follows.
Where do we hire?
We are a remote first company and we hire ONLY in the following states: AZ, CO, FL, GA, ID, IL, KS, MA, MI, MN, MO, NH, NY, NC, OH, OR, PA, SC, TN, TX, UT, VA, WA, and WI.
About the role.
The Staff Data Scientist is the senior individual contributor responsible for Medbridge's predictive and statistical models. The core problems are hard and consequential: comparing patient outcomes fairly across providers when patients differ, predicting what is likely to happen next, estimating whether a product or program changed anything, and, over time, recommending what to do. You own each problem end to end: framing it with product and clinical partners, choosing the method, building and validating the model, shipping it to production in Snowflake, monitoring and retraining it, and measuring whether it is used and what it changed. The role leads through influence rather than direct people management.
In this role you will:
- Own the design, development, validation, and production operation of Medbridge's predictive and risk-adjustment models, beginning with the models behind our outcomes benchmarking product.
- Set the methodological bar: choose approaches that fit the question and the data, validate to a standard that holds up to outside expert scrutiny, and document decisions so partners understand and can question them.
- Write production-quality code: tested, versioned, reviewed, and reproducible, and own its deployment and monitoring in Snowflake in partnership with Data Engineering.
- Estimate whether Medbridge products, programs, and content change patient outcomes, using experimental and observational methods, and shape the instrumentation that makes those estimates possible.
- Turn ambiguous goals into well-posed problems with options and tradeoffs, work directly with product, engineering, clinical, and customer-facing teams to get models into the product, and establish the team's practices for evaluation, documentation, monitoring, and responsible use of healthcare data.
What you will need to succeed:
- A track record of models that ran in production and held up: you have owned model code through deployment, monitoring, and retraining, and you can describe what broke and how you fixed it.
- Statistical depth you use every day: you reason about how the data were generated, recognize when a model's assumptions fail, quantify uncertainty, and can explain why one method fits a problem and another does not.
- Strong Python and SQL and disciplined software engineering, including testing, version control, code review, and reproducible pipelines. Snowflake experience is strongly preferred.
- Experience with messy real-world healthcare data, including product data used by clinicians, administrators, or patients, and working knowledge of causal inference and evaluation design.
- Experience taking a model from an open-ended business goal to a shipped product feature in partnership with product and engineering, and comfort being the first person on a problem.
- Judgement under constraint: when the data, timeline, or requirements are not what the ideal method assumes, you find a path to a defensible solution, explain the tradeoffs plainly, and know what would let you strengthen it later.
- Clear communication with clinical, product, and leadership audiences, including plain explanations of uncertainty, limitations, and tradeoffs, and a commitment to trustworthy, ethical use of healthcare data.
- Curiosity about new methods and tools, paired with the judgment to evaluate them before adopting them.
- A degree in statistics, biostatistics, computer science, economics, or another quantitative discipline, or equivalent capability developed through another professional path. We welcome nonlinear backgrounds and evaluate demonstrated scope and impact rather than relying on a specific credential or number of years.
What would make you stand out:
- Experience with patient-reported outcome measures, risk adjustment, or outcomes benchmarking in a provider, payer, or quality-reporting setting.
- Experience with electronic health record (EHR), electronic medical record (EMR), or claims data.
- Experience building recommendation systems and measuring their adoption and downstream impact.
- Experience evaluating generative AI applications in a regulated domain (such as healthcare), including efficacy and safety review.
- Model documentation and validation that has withstood external review, such as diligence, audit, regulatory submission, or peer review.
- Experience as the first data science or ML hire in an organization.
Our Values:
Excellence: A job worth doing is a job worth doing RIGHT.
- We believe that excellence is never an accident. It’s the result of high intention, sincere effort, and intelligent execution.
- We are change-makers who push the boundaries of what is possible, continually reimagining patient care.
- We realize that our work impacts people’s health and demands that we hold ourselves to the highest possible standards.
- We know a good thing when we see it; when exceptional talent comes our way, we hire them and help them grow.
- We understand the value of time, and we give our best effort everyday because we have a day in which to give it.
Fortitude: Our goals are big. Our dedication is bigger.
- We embrace ambitious and challenging projects with confidence in our ability to achieve them together.
- We are courageous as we venture into the unknown.
- We persevere in the face of difficulties.
- We do not let perfection be the enemy of progress; we focus on taking the next best step forward.
- We take ownership of our mistakes and of our successes, and we learn from both.
Service: We work for something more important than ourselves.
- We care deeply about our colleagues, customers, and patients, ensuring our work at Medbridge has a lasting impact.
- We take a multi-disciplinary, data-oriented approach to solving problems.
- We lead with confidence and humility, embracing a servant-leadership mindset as we support and challenge one another to reach our shared goals.
- We know that if we do great work, we help people live healthier lives.
Salary Range:$190,000 to $220,000 base salary.
At Medbridge, salary ranges are assigned to a job based on 3rd party salary benchmark surveys. Individual pay within this range is informed by the candidate's skills, capabilities and experience.
We embrace diversity and are an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. No matter your background, your orientation, your identity expression, or whatever else makes you unique, if you want to raise the bar and join an amazing team of passionate people, then we'd love to work alongside you at Medbridge.
Take the Next Step: If you’re excited about this opportunity and think Medbridge is the right fit for your career, apply now! Our Talent Acquisition team will follow up with you shortly.
Please note: Due to reports of phishing, we're requesting that all Medbridge applicants apply through our official careers page at https://www.medbridge.com/careers. All official communication from Medbridge will come from email addresses ending with @medbridge.com.
Salary Context
This $190K-$220K range is above the 75th percentile for Data Scientist roles in our dataset (median: $167K across 150 roles with salary data).
View full Data Scientist salary data →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 8,943 AI roles we're tracking, Data Scientist positions make up 4% of the market. At MedBridge, 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 $190,000 based on 1,406 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($205K) sits 8% above the category median. Disclosed range: $190K to $220K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
MedBridge AI Hiring
MedBridge has 1 open AI role right now. They're hiring across Data Scientist. Based in Bellevue, WA, US. Compensation range: $220K - $220K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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