[![Image]() Meet Jobicy Copilot — free AI autofill for job applications + remote job alerts ›](#) [All remote jobs](https://jobicy.com/jobs.md)Open role[![Bayesian Health logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2025/08/f74593d8-221.png)](https://jobicy.com/company/bayesian-health.md)Remote opportunity at[Bayesian Health](https://jobicy.com/company/bayesian-health.md) # Senior/Staff Machine Learning Data Scientist Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/bayesian-health.md)Share8 Sep 2026Published37Listing views2Application actions8 Oct 2026Apply before Opportunity details ## About this role. AI SummaryBayesian Health is seeking a senior or staff-level machine learning data scientist to build and own real-time clinical prediction models from prototype through production. The role combines applied machine learning, production Python and SQL development, data mapping, AWS debugging, and evaluation of model performance in live healthcare workflows. The hire will work closely with clinical, engineering, and product partners to define features, prioritize work, and communicate model outcomes to technical and non-technical audiences. This is a high-impact early-stage healthcare AI position focused on improving point-of-care clinical decisions and patient outcomes. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThe position requires a PhD plus substantial experience shipping ML products, with ownership spanning model design, production deployment, clinical data investigation, and real-world impact measurement. Working with messy EHR data and time-sensitive clinical systems raises both technical complexity and the stakes for quality and reliability. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job medianMarket rate$220,000US market range$185k–$270k0$297k AI insightNo salary is disclosed in the posting. Estimated US annual base salary for a Senior/Staff Machine Learning Data Scientist in an early-stage healthcare AI company is approximately $185,000-$270,000 USD, with a midpoint estimate of $220,000; actual total compensation may vary based on staff level, location, equity, and healthcare-domain experience. ## Core skills Skills and capabilities most closely associated with this opportunity. [Machine Learning](https://jobicy.com/jobs?search_keywords=Machine%20Learning.md)[Data Science](https://jobicy.com/jobs?search_keywords=Data%20Science.md)[Healthcare AI](https://jobicy.com/jobs?search_keywords=Healthcare%20AI.md)[Clinical Data](https://jobicy.com/jobs?search_keywords=Clinical%20Data.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[SQL](https://jobicy.com/jobs?search_keywords=SQL.md)[AWS](https://jobicy.com/jobs?search_keywords=AWS.md)[MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md)[Time Series Forecasting](https://jobicy.com/jobs?search_keywords=Time%20Series%20Forecasting.md)[Model Deployment](https://jobicy.com/jobs?search_keywords=Model%20Deployment.md) Sample interview questionsDescribe a machine learning model you owned from initial prototype through production deployment.I would outline the clinical or business problem, data validation and labeling approach, model-selection process, offline metrics, production architecture, monitoring plan, and the measured impact after launch. I would also explain how I handled drift, failures, and iteration based on real-world feedback. How would you evaluate a real-time clinical prediction model beyond standard offline accuracy metrics? I would assess calibration, sensitivity, specificity, precision-recall performance, timeliness of predictions, alert burden, subgroup performance, and workflow adoption. I would pair these with prospective monitoring and clinician feedback to ensure the model improves decisions rather than only achieving strong retrospective metrics. How do you approach messy EHR data when developing a new model? I start with source-system mapping, data lineage, timestamp semantics, missingness analysis, and clinician-informed definitions of labels and features. I build reproducible transformation pipelines, document assumptions, test edge cases, and validate a sample of patient records end to end before trusting aggregate results. Tell us about a time you debugged a production ML issue involving data or prediction quality. I would describe tracing the issue from an anomalous output through logs, feature generation, source data, and deployment versions. A strong response would include a root-cause fix, added automated checks or monitoring, stakeholder communication, and evidence that the corrective action prevented recurrence. How would you communicate model performance and limitations to clinicians and non-technical stakeholders? I would lead with the clinical decision the model supports, use clear outcome-oriented metrics and representative cases, and explicitly state uncertainty, intended use, and failure modes. I would avoid unnecessary technical jargon while providing deeper documentation and methodology for audiences who need it. ### Senior/Staff Machine Learning Data Scientist ### In Brief * We’re an early-stage startup on a mission to make healthcare proactive by empowering physicians, nurses, and care team members with real-time data to save lives. * TLDR: Independent end-to-end model development with ability to work cross-functionally with Clinical, Engineering, and Product to clarify and prioritize specifications and features for our life-saving AI models. ### Who We Are Bayesian Health’s mission is to improve patient outcomes by empowering clinicians with the insights they need to make the right decision for the right patient at the point-of-care. We’re a diverse team of clinicians, engineers, machine learning experts, product designers, and performance improvement leaders committed to enabling smarter, patient-specific care delivery through unlocking the power of data. We’re funded by top tier tech and biotech investors: Andreessen Horowitz, American Medical Association’s venture arm, Catalio Partners, and LifeForce Capital. Our company has won many awards; most recent recognitions include: Forbes AI Top 50, World Economic Forum Tech Pioneer, Time Best Inventions, BioTech AI Company of the Year. [Read more about our recent publication in Nature Medicine that associates our products with lives saved.](https://www.bayesianhealth.com/sepsis-research/?__hstc=251652889.1e7598f5de0ed9b513da684474e8a044.1710861879595.1710945070565.1710971833587.3&__hssc=251652889.1.1710971833587&__hsfp=1890770568) ### What you’ll do As a Senior/Staff Machine Learning Data Scientist, you are not satisfied with training and tuning ML models that predict clinical conditions in patients; you also want to own the effectiveness of your model in the real world. In practice, that means you aren’t afraid to get your hands dirty by writing data mapping code, debugging a specific patient case by following patient data as it moves through our AWS services, or improving the timeliness of your model’s predictions by reading and writing production-grade Python and SQL code. Responsibilities * Model Prototyping: Develop and tune innovative, new ML models and labeler systems based on deep understanding of clinical use cases and state-of-the-art ML methods * Productionizing: The same models that you develop with production-grade Python * Deploying: Identify strategies for improving our production ML-based systems, and write, debug, and deploy production-grade Python code to implement those strategies * Cross-Functional Alignment: Data Science for storytelling – understand model performance and metrics, and present this to technical and non-technical users, both internally and externally ### Minimum qualifications * Ph.D. in a relevant field plus 3+ years experience shipping ML based software products * Experience owning your ML models from prototyping to production, especially real-time algorithms that update dynamically across time * Experience writing production-grade Python and SQL code to implement and evaluate ML models in production systems * Track record of using statistics and performance metrics to compare end-to-end ML and product performance ### Preferred qualifications * Experience shipping breakthrough or 0-1 products from end to end, interpreting and leveraging State-of-the-Art methods to do so * Experience using messy clinical and health data to design new products for large Health Systems * Experience with any of the following: PyTorch, PySpark, HL7, FHIR, EHR, time series data, signal processing, MLFlow, anomaly detection, Bayesian statistics, quantile regression, time-series forecasting * You bring passion and enthusiasm to your work, and are excited to join a growing team to Get Stuff Done and save lives! Bayesian Health provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training. Show more [Apply now >](https://jobicy.com/jobs/152824-senior-staff-machine-learning-data-scientist.md) > Annual salary information is not provided for this position. 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